A Digital Image Watermarking Method in the Discrete Cosine Transformation Domain

In this paper, a watermarking method has been proposed based on Discrete Cosine Transform(DCT) which can be used in order to protect copyrighting and to provide right of image ownership. In this method, the original image transferred to DCT domain after dividing into non-overlapped blocks 8×8 and to the same method, watermark image which can be whether a firm mark or any desired image from owner of the art work, after dividing into non-overlapped blocks 4×4, transferred to DCT domain. Watermark image coefficients after one step coding composed with low frequency coefficients of original image and create the final watermark image. On the other hand, the process of reforming watermarked image and extracting the original watermark on the secondary side is extractable by using original image and with reverse mechanism. Experiments show that this method in encountering with a number of routine attacks has a good resistance

Trajectory Generation for Hip Rehabilitation Exoskeleton Using Trajectory

A walking rehabilitation exoskeleton robot is used for patient having walking difficulty to undergo walking therapy by wearing it on his lower body. If the rehabilitation is done directly by using  predefined normal gait, the patient can feel discomfort and may lead to painful therapy. This can also endangered the patient limb under therapy. Due to that reason, the patient will be discouraged and unmotivated to proceed with the therapy. This paper presents the hip trajectory generation by utilizing the patient current trajectory that morphs into the target trajectory by generating a sequence of gradually changing hip trajectories

Determining Best Window Size for an Improved Gabor Transform in EMG Signal Analysis

Electromyography EMG is a standout amongst the most regularly utilized tools to study human muscle condition. But due to the intricate attributes of the EMG itself, time-frequency distributions such as Gabor transform and spectrogram are more preferred than the simpler time distribution and frequency distribution. These techniques have been broadly utilized as it can provide both time and frequency information. However, both techniques have a fix window size for all frequency values, thus there exist a problem of determination of the window size, where excessively limit window and too wide window, will result in poor frequency resolution and time resolution, respectively. Along these lines, the point of this study is to choose the best window size so as to be utilized with Gabor transform to screen human muscle activity during core-lifting task. Four electrodes were placed on the right and left biceps brachii, and left and right erector spinae. In this study, the results of five acceptable window sizes (300, 400, 430, 450 and 520) were shown, despite the fact that other window sizes were also tested. Three criteria have been considered during the determination of the best window size, which are good time resolution, good frequency resolution, and high accuracy. Results demonstrate that window size of 450 is the best compared to others. As an additional analysis, the result is compared to a spectrogram and it can be seen that Gabor transform is better, as it has the flexibility in choosing the window size, thus affects the resolution and accuracy

Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network

The aim of this paper is to propose an exploratory study on simple, accurate and computationally efficient movement classification technique for prosthetic hand application. The surface myoelectric signals were acquired from 2 muscles—Flexor Carpi Ulnaris and Extensor Carpi Radialis of 4 normal-limb subjects. These signals were segmented and the features extracted using a new combined time-domain method of feature extraction. The fuzzy C-mean clustering method and scatter plots were used to evaluate the performance of the proposed multi-feature versus other accurate multi-features. Finally, the movements were classified using the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network. Comparison results indicate that ANFIS not only displays higher classification accuracy (88.90%) than the artificial neural network, but it also improves computation time significantl

Profile Reconstruction Utilizing Forward-Backward Time-Stepping With the Integration of Automated Edge-Preserving Regularization Technique for Object Detection Applications

A regularization is integrated with Forward-Backward Time-Stepping (FBTS) method which is formulated in time-domain utilizing Finite-Difference Time-Domain (FDTD) method to solve the nonlinear and ill-posed problem arisen in the microwave inverse scattering problem. FBTS method based on a Polak-Ribiète-Polyak conjugate gradient method is easily trapped in the local minima. Thus, we extend our work with the integration of edge-preserving regularization technique due to its ability to smooth and preserve the edges containing important information for reconstructing the dielectric profiles of the targeted object. In this paper, we propose a deterministic relaxation with Mean Square Error algorithm known as DrMSE in FBTS and integrate it with the automated edge-preserving regularization technique. Numerical simulations are carried out and prove that the reconstructed results are more accurate by calculating the edge-preserving parameter automatically

Artificial neural network-based kinematics Jacobian solution for serial manipulator passing through singular configurations

Singularities and uncertainties in arm configurations are the main problems in kinematics robot control resulting from applying robot model, a solution based on using Artificial Neural Network (ANN) is proposed here. The main idea of this approach is the use of an ANN to learn the robot system characteristics rather than having to specify an explicit robot system model. Despite the fact that this is very difficult in practice, training data were recorded experimentally from sensors fixed on each joint for a six Degrees of Freedom (DOF) industrial robot. The network was designed to have one hidden layer, where the input were the Cartesian positions along the X, Y and Z coordinates, the orientation according to the RPY representation and the linear velocity of the end-effector while the output were the angular position and velocities for each joint, In a free-of-obstacles workspace, off-line smooth geometric paths in the joint space of the manipulator are obtained. The resulting network was tested for a new set of data that has never been introduced to the network before these data were recorded in the singular configurations, in order to show the generality and efficiency of the proposed approach, and then testing results were verified experimentally