Active magnetic bearing (AMB) is a suspension system to levitate a rotating shaft freely without any physical contact which allows extremely fast rotation speeds. One big control challenge of the AMB systems, which appears during high rotation speeds, is the non-uniform distribution of the rotor weight about its rotating axis. This is usually referred to as the rotor imbalance problem which produces sinusoidal disturbance forces. These disturbances lead to undesirable vibrations and large deviations of the rotor shaft from its desired trajectories. We adopt in this work model predictive control (MPC) to reduce the effect of these sinusoidal disturbances and to achieve a stable levitation of the rotor shaft while tracking a reference trajectory. Owing to the MPC capability of handling constraints in an optimal manner, physical input constraints can be committed. Moreover, state 

Recently, Wireless Body Area Network (WBAN) has revolutionized e-health-care. WBAN boosts monitoring vital signs utilizing tiny wireless sensors implanted in or around the human body. In February 2012, the IEEE 802.15.6 WBAN standard was released for low-power and short-range communication around the human body. The standard defines one medium access control layer and three different physical layers: narrow band , ultrawideband, and Human Body Communication (HBC) layers. We are motivated by exploiting the human body as a communication medium. We propose a novel optimized architecture for the HBC baseband transceiver based on deep learning. The receiver utilizes two deep neural networks: one for frame synchronization to recover data and timing precisely and the other for the channel decoder to improve transceiver performance and reduce power consumption. In addition, low-complexity Preamble/SFD generator, Walsh modulation, and FSC spreader modules are proposed to reduce the power consumption while preserving the transceiver performance. Compared with the traditional hard-decision channel decoder, the proposed neural network decoder improves the block error rate by 2 dB. The proposed HBC transceiver supports 1.312 Mbps data rate at 42 MHz clock rate. The transceiver is implemented in RTL and synthesized on 90 nm CMOS technology. It consumes 493 pJ/bit on the receiver side and 105 pJ/bit on the transmitter side.

The control of flexible robot manipulators is a challenging task, especially when one considers parallel and interconnected manipulators under flexibility considerations. This paper proposes a method to estimate the position of the end-effector of a flexible interconnected manipulator based on a virtual sensor principle and function approximation schemes. By using SolidWorks/MSC ADAMS software, we developed a virtual prototype of a flexible interconnected manipulator, and rigorously evaluated the feasibility of using function approximation schemes such as Neural Networks (NN), Support Vector Machines (SVM), and Gaussian Process (GP) in estimating the deflection error arising due to the flexibility of the robot structure. Our rigorous computational experiments have shown that: (1) the NN, SVM, and GP models were are able to attain the promising and reasonable prediction accuracy, (2) a feedforward NN with 535 neurons and an Ascending distribution of its nodes achieves the best prediction and generalization to unseen environments (the upper bound of the error was 0.15 × 10−3 m); implying the robust estimation of the position of the end-effector under flexibility considerations, and (3) the control based on the inverse Jacobian and a NN-based estimator was able to follow a sinusoidal trajectory with reasonable tracking and error performance in MSC ADAMS & MATLAB/Simulink co-simulation. Our results show the feasibility and effectiveness of the nonlinear relationships learned by NN, SVM, and GP in aiding estimation and control of the position of the end-effector of the flexible manipulator with a promising/desirable capability.

Manual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies have been conducted on jacquard fabrics despite their important and intriguing nature. Previous studies on jacquard fabrics are of simple patterns. This paper introduces a new and novel field in fabrics defect detection. Complex-patterned jacquard fabrics are much more challenging. In this paper, novel defect detection models for jacquard-patterned fabrics are presented. Owing to the lack of available databases for jacquard fabrics, we compiled and experimented on our own novel dataset. Our dataset was collected from plain, undyed jacquard fabrics with different complex patterns. In this study, we used and tested several deep learning models with image pre-processing and convolutional neural networks (CNNs) for unsupervised detection of defects. We also used multispectral imaging, combining normal (RGB) and near-infrared (NIR) imaging to improve our system and increase its accuracy. We propose two systems: a semi-manual system using a simple CNN network for operation on separate patterns and an integrated automated system that uses stateof-the-art CNN architectures to run on the entire dataset without prior pattern specification. The images are preprocessed using contrast-limited adaptive Histogram Equalization (CLAHE) to enhance their features. We concluded that deep learning is efficient and can be used for defect detection in complex patterns. Proposed method of EfficientNet CNN gave high accuracy reaching 99% approximately. We also found that multispectral imaging is more advantageous and yields higher accuracy

In this paper, we propose a frequency-reconfigurable antenna structure consisting of a dielectric resonator (DR) topped by a superstrate material. Two metasurfaces (MSs) are placed upon the DR and the superstrate, where these two MSs are utilized to synthesize a localized reduction of the dielectric constant of the DR. By placing switches into one of the MSs, the distribution of dielectric constant of the DR can be switched to one of two predefined distributions, which is equivalent to switching the DR length to two different lengths. Consequently, the frequency response of the proposed structure can be tuned to one of two operating bands. The excited modes inside the proposed antenna were obtained analytically and through simulations. Also, the dielectric constant value of substrates topped by MSs was analyzed. 

A breast cancer hyperthermia system has been developed for the targeted of increasing hyperthermia therapy efficiency after lumpectomy surgery with the assistance of fat grafting technique. Enhancing the efficiency of hyperthermia treatment will decrease the required radiation doses after surgery. A fat grafting technique is applied around the tumor region that is located into glandular tissue for the purpose of enhancing wave penetration through glandular tissue. The proposed hyperthermia system employs a single horn antenna operating at 2.45 GHz. Here, a modified hyperthermia system was tested on a hemisphere phantom of two different densities with various tumor sizes at depth 2.8 cm from skin layer. Transmission and reflection coefficients of applied electromagnetic waves at the air‐phantom interface were calculated analytically.

The increasing electrical power generation from renewable energy resources poses new challenges to the electrical grid. These challenges are mainly related to the inconsistent nature of renewable energies and the burden on transmission lines due to the increasing generated power. In this paper, the correlation between renewable energy generation and the transmission capability of electrical power lines is studied. In order to facilitate the accommodation of renewable energies the actual dynamic line rating (DLR) is estimated based on actual weather conditions. To achieve this purpose a new DLR estimation method using machine learning is presented. Line rating depends on various meteorological factors (wind speed, wind direction, ambient temperature, and solar radiation). A machine-learning model was trained using Random Forest (RF) regressor to estimate the rating of a transmission line 

This work introduces a new topology for designing low-phase noise (PN) dual-band voltage-controlled oscillator (VCO) by proposing orthogonally located inductors in 0.18-m CMOS. The inductors are implemented using five metal layers keeping the lowest layer empty to maximize the quality ( factor. The first inductor is two halves shunted octagonal loops using the top layer (M and utilized in cross-coupled VCO, while the second inductor is formed by four C-shaped shunted inductors using the lower four layers M and used in current-reuse (CR) VCO. The M inductor improves the -factor by more than 25%over one loop inductor in the frequency band of interest, while the M inductor uses four shunt layers to boost the -factor by 28% in -band compared to the single-layer inductor. The VCO oscillates from 22.36 to 23.4 GHz with PN of 112.4 dBc/Hz at 1 MHz and figure of merit (FoM) of 188.8 dBc/Hz

The fifth-generation (5G) wireless cellular networks aim to increase the users capacities and quality of experience as well as integrating different wireless bands and modes of access. The introduction of cognitive radio technology and heterogeneous networks (HetNets) as part of the architecture of the 5G aims to efficiently reuse the available spectrum. This paper presents a two-stage framework based on matching theory and auction games with the objective of efficiently admitting secondary users in the wireless scene. At the first stage, a fast convergence matching game for the users admission problem in 5G HetNets is considered, where secondary users are associated to appropriate secondary base stations. These base stations access the available primary spectrum on behalf of its associated users in the next stage, namely a repeated modified English auction