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International audience; Cable-Driven Parallel Robots (CDPRs) have several advantages over conventional parallel manipulators most notably a large workspace. CDPRs whose workspace can be further increased by modification of the geometric architecture are known as Reconfigurable Cable Driven Parallel Robots(RCDPRs). A novel concept of RCDPRs, known as Mobile CDPR (MCDPR) that consists of a CDPR carried by multiple mobile bases, is studied in this paper. The system is capable of autonomously navigating to a desired location then deploying to a standard CDPR. In this paper, we analyze the Static equilibrium (SE) of the mobile bases when the system is fully deployed. In contrast to classical CDPRs we show that the workspace of the MCDPR depends, not only on the tension limits, but on the SE constraints as well. We demonstrate how to construct the Available Wrench Set (AWS) for a planar MCDPR wih a point-mass end-effector using both the convex hull and Hyperplane shifting methods. The obtained results are validated in simulation and on an experimental platform consisting of two mobile bases and a CDPR with four cables.

Recent advancements in the domain of robotics have offered support to humans in their everyday activities with the aim of offloading workers from performing repetitive tasks. The present work highlights one such task in garment industry where the robot may find potential to manipulate different garments including developing a number of robotic skills like laundry pile sorting, garment stacking and garment folding/ unfolding. This paper is aimed to study the integration of hardware and software developed for ClopeMa Project on a human-friendly robotic platform, i.e. a Baxter robot that can safely operate side by side with humans. In particular, the paper discusses integration of RGB-D sensor with the ROS environment and studies utility of garment manipulation. The goal is to present a working platform which can autonomously recognize the configuration of a piece of garment spread out on a flat surface. The algorithm for recognizing the garment consists of first applying Gaussian mixture model (MoG) for background subtraction and then using polygonal approximation to acquire feature points for the foreground of the garment. The proposed algorithm is tested online through series of experiments on towel, pants and t-shirts of various colors and materials. Results under varying lightening conditions witness robustness of the proposed scheme.

Data mining and machine learning methods are proposed in order to discriminate between various fabrics. In particular textile classes are distinguished, like awning, jeans, jute, pile and satin. The real signals are acquired by a laboratory setup that includes: a Cartesian robot with ability to apply controlled constant pressure and speed, a MEMS piezo capacitive sensor and a Simulink module for signal recording. A set of static and dynamic features is extracted from the data series. A novel approach to feature selection is designed, based on an iterative p-value filter, with separate runs (and results) for different pairs of classes. A set of one-to-one class classifiers (a support vector machine) is learned in corresponding feature spaces. The evaluation procedure, in terms of a ten-fold cross validation, confirmed a 100% of classification accuracy of the proposed approach on available sensor data.

The total installed capacity of wind turbines is continuously increasing with focus on Renewable Energy Sources (RES). The reliability and efficiency of wind turbines has become a major issue. The occurrence of fault in various components of wind turbine needs to be addressed for improved performance. Among these issues, wind turbine bearing fault is the most significant. Different techniques such as online monitoring using Artificial Intelligence (AI), have been proposed and still research is being carried out in this domain. This paper presents a fault diagnosis analysis of main shaft bearings of wind turbines. The goal is to monitor the condition of wind turbines for early fault prediction so that the turbine can immediately be adjusted for improved performance and extended service life. Different techniques such as Fast Fourier transform (FFT), Hilbert Huang Transformation (HHT), Feature extraction, Logistic Regression (LR) are applied on a real data set of 18 wind turbines to accurately evaluate the health of the turbine. The results highlight the superior and reliable performance of these techniques for bearing fault detection for cost effective operation and maintenance (O&M).

Demand of power is increasing worldwide and this increased demand has motivated the engineers to look for alternative or renewable energy systems. Meanwhile, the data centers are also employing the solar energy systems to avoid the AC/DC power conversion losses due to the fact that an efficient solar system with DC/DC converter will help them to save up to 15% of the power in comparison to the conventional power supply system using non-renewable energy sources. The efficiency of the solar energy systems is greatly dependent on the DC/DC converters and hence, there is a need to study the comparison of efficiency of such converters which can be used in Switch Model Power Supplies (SMPS). This paper provides a comparative analysis between some of the DC/DC converters i.e. synchronous buck converter, isolated buck converter and non-isolated buck converter. Moreover, the important factors such as input voltage variation and the effects of open and closed loop systems of mentioned DC/DC converters are also explained.

Today, variable speed pumped storage (VSPS) system is an emerging technique, being used in hydro powered plants to increase the efficiency, quality and control of the power of the grid. The doubly fed induction machine (DFIM) based VSPS systems are gaining more importance because of the cheap price of the converter i.e. back to back power electronics converter. This paper discuses an efficient method for the rotor side converter (RSC) to control the active and reactive power flowing through the stator of DFIM while the grid side converter (GSC) has been used to maintain the DC voltage level. This paper also discusses the VSPS systems Worldwide and its comparison with hydro powered projects which are recently developed in Pakistan. This paper suggests the use of VSPS systems in Pakistan to increase the efficiency of the conventional hydro power projects.

Power electronic converters play a vital role in a power system and the most commonly used type of DC/DC converters is buck converter. This is due to the fact that buck converters are widely used to step down the voltage as well as they provide good voltage regulation in critical applications including power supplies for data centers. Therefore, designing a buck converter that gives a regulated output, acceptable for switch mode power supplies is still challenging task. A closed loop buck converter mitigates the problems such as input voltage unstability or variation. This paper discusses the effects of input voltage variations on the open loop and closed loop models of buck converter. Voltage Mode Control (VMC) has been proposed to develop an efficient closed loop system in order to avoid voltage fluctuations at the output of the buck converter.

Artificial robotic hands are designed having dexterity and functionality as close as the natural human hand produce. This paper proposes a bio-mechatronic approach for the design and control of an anthropomorphic artificial hand capable of performing basic human hand motions with fundamental gripping functionality. The mathematical model is derived using forward kinematics and also simulated on MATLAB® to ascertain the position of robotic fingers in 3D space. The dexterity of the artificial hand is exhibited by imitating the natural motion of the human fingers. Imitation is achieved by two different methods; a camera based marker recognition system to identify the human hand gestures and acquired flexion data from sensors attached to the human fingers. In order to have proper gripping, closed-loop control is implemented using the tactile sensors. Feedback for the closed-loop control is provided by force sensing resistors (FSRs), attached on the fingertips of the robotic hand. These sensors also enable handling of fragile objects.

The alpha activity of brain has a frequency range of 8-12 Hz. In order to extract the alpha activity from EEG data we propose a new procedure based on independent component analysis (ICA), which can incorporate the a prior frequency information into the ICA to extract independent components in the desired frequency range. We named this procedure as augmented ICA (Au-ICA). The performance of the proposed procedure for alpha extraction has been compared to that of the conventional band-pass filtering via the scalp alpha power maps and cortical source maps of the alpha activity. Our results demonstrate that the alpha power maps and cortical source maps obtained with our method reveal more localized alpha generating regions of brain as compared with the conventional methods. Furthermore they match more closely to the activated regions of brain, mapped using functional magnetic resonance imaging (fMRI) thus validating our results. We believe that the Au-ICA is a more effective method of extracting brain activity reflected in the specific frequency range of EEG signals. The results presented here emphasize that the proposed method may be used for accurate source localization or imaging maps from EEG signals.

In this paper, we have attempted forearm movement tracking in 3-D by estimating joint angles of a forearm with an inertial measurement unit (IMU) consisting of a triaxis accelerometer and gyroscope sensors. As the feedback of the forearm motion tracking, we have implemented a 3-D digital forearm model based on the kinematic chain theory utilizing super-quadric surfaces and joints, and attempted to control the digital arm with the estimated joint angles via Kalman filtering. Some preliminary experimental results are presented.

Human-computer interaction requires efficient acquisition of the relevant information from the user. This poses high accuracy in the performance of the relevant systems, especially when visual information is used as the basic means to capture the user's information. At the same time, speed and efficiency under a variety of conditions (e.g, resolution, luminance) is required. From this perspective, a new approach in the automated iris and gaze detection problem is presented here, using the chrominance of low resolution webcam data. The algorithms developed, when combined with a face tracker, exhibit high performance, circumventing the variability of the lighting conditions and/or user's facial characteristics.