Fundamental study on the circular polarization antenna for RFID UHF reader

A few years after the early investigation on ultra-high frequency (UHF) wireless system, considerable research efforts have been put into the design of UHF antennas for radio frequency identification systems. These UHF antennas are essential for providing wireless communications based on the use of very narrow pulses on the order of nanoseconds, covering a small bandwidth in the frequency domain, and over very short distances at very low power densities. In this project, new models of rectangular, T slotted and angular slot UHF antennas are proposed by studying their parameters of antennas such as return loss, gain, radiation pattern and voltage standing wave ratio. The wideband behavior is due to the fact that the currents along the edges of the slots introduce an additional resonance, which, in conjunction with the resonance of the main patch, produce an overall frequency response characteristic. These antennas are considerable small than others listed in the references, which their sizes are less than a wavelength, compact, and suitable for many RFID applications. The configuration of slots type for both patches and pin feed are considered as a novelty and contribution in this project. The geometry of the antenna implies the current courses and makes it possible to identify active and neutral zones in the antenna, thus it will be possible to fix which elements will act on each characteristic. This project also investigated the ability of slotted UHF antennas to improved read range and gain between tags and UHF RFID Reader within the same field test environment. Inserting a half-wavelength slot structure with additional small patches gap attached have resulted frequency notched band characteristics. The measured return loss, radiation patterns, and phase agree well with the simulated results. The antenna provides a directional pattern with the return loss less than -10 dB and circular in phase

Painful Pes Anserine Bursitis Following Total Knee Replacement Surgery: Two cases

Pes anserine bursitis (PAB) is an inflammation of the bursa located between the medial aspect of the tibia and the hamstring muscles. It is common in patients with degenerative or inflammatory knee arthritis, usually has a self-limiting course and tends to respond well to conservative treatment. However, painful PAB directly following total knee replacement surgery is rare. We report two such cases who were diagnosed via ultrasonography at the Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia, in 2015. Both patients were treated locally with triamcinolone acetonide under ultrasound guidance and responded well to treatment

The mediating effect of brand image between electronic word of mouth and purchase intention in social media

Instagram is one of the fast growing social media platform, however studies related to the consumers’ purchasing behavior involving Instagram particularly in the Malaysian context is less emphasized. In social media, brand image of a product is important as it can enhance knowledge of consumers about the product and facilitate them to commit in purchasing behavior. Therefore, its role as mediator between electronic word of mouth (e-WOM) and purchase intention is important to research. A self-administered survey was carried out by distributing online questionnaires to the Instagram users using convenience sampling method. The results of SEM revealed that e-WOM factors positively and significantly affect brand image and purchase intention, and purchase intention of Instagram’s users is positively influenced by the brand image. It was found that the relationship between e-WOM factors and purchase intention is partially mediated by brand image. This research provides a useful model of e-WOM, brand image and purchase intention for determining how consumers behave in communicating and reviewing products in Instagram which gives impact to their purchase intention. Theoretical and practical implication of the study was discussed

A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method

In this work, a new constitutive model of a magneto-rheological fluid (MRF) actuator is proposed using an extreme learning machine (ELM) technique to enhance the prediction accuracy of the field-dependent actuating force. After briefly reviewing existing rheological constitutive models of MRF actuator, ELM algorithm is formulated using a single-hidden layer feed-forward neural network. In this formulation, both the magnetic field and measured shear rates are used as inputs variables, while the shear stress predicted from the ELM training is used as an output variable. Subsequently, in order to validate the effectiveness of the proposed model, the target defined as the error between the prediction and measured data is set. Then, the fitness of the training and prediction performances is evaluated using a normalized root mean square error (NRMSE) method. It is shown that the shear stress estimation based on the ELM model using sinusoidal activation function is more accurate than conventional rheological constitutive models such as Herschel-Bulkley model. It is also demonstrated that the proposed model is capable of predicting the field-dependent yield stress which is defined as an actuating force of the MRF actuator without causing significant errors

An analysis of a flexible dry surface electrodes

In the medical field, electrodes are commonly used either to retrieve signals or to conduct current. Most of the off-the-shelf surface electrodes are made from metal or rigid substrates. This paper presents a work on designing a new flexible dry electrodes using poly (3,4-ethylenedioxythiophene) polystyrene sulfonate and silver by means of dispenser printing technology. The polyester cotton fabric was selected as the substrate in this electrode designed. To analyse the new proposed composites of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate and silver, different mixtures have been applied. Results from the experiment show that the conductivity of the proposed flexible electrode is comparable with the commercialized pre-gelled electrode when applied to an electrical stimulator device. Eight out of ten subjects under test described no difference in comfort between the proposed electrodes and pre-gelled electrodes

Lateral control with neural network head roll prediction model for motion sickness incidence minimisation in autonomous vehicle

Generally, the passengers of an autonomous vehicle suffer substantial motion sickness (MS) compared to the driver. This particularly occurs during cornering as the passengers are inclined to tilt their heads in the direction of lateral acceleration whereas the driver tends to tilt their head in the opposite direction. Therefore, it is crucial for the passengers to reduce the head roll angle towards the direction of lateral acceleration to decrease the susceptibility to MS. This study proposed a lateral control approach based on the head roll angle which was estimated by head roll prediction models to reduce the severity of MS in an autonomous vehicle. The prediction models were developed via the Artificial Neural Network (ANN) technique. A Proportional-Integral (PI) controller was implemented to produce a corrective wheel angle based on the predicted head roll angle responses of the driver and passenger. The corrective angle caused a decrease in the lateral acceleration. The decrement in lateral acceleration then reduced the passenger’s head roll angle towards the direction of lateral acceleration. The findings indicated that the suggested control approach was capable to decrease the MS Incidence (MSI) index by 5.97% over a single lap and 14.48% over 10 laps

Accurate and fast estimation for field-dependent nonlinear damping force of meandering valve-based magnetorheological damper using extreme learning machine method

The application of artificial neural network (ANN) models in magnetorheological (MR) damper has gained interest in various studies because of the high accuracy in predicting the damping force, especially for control purposes. However, the existing neural network models have apparent drawbacks such as relatively long training time and the possibility to be trapped in local solutions. Therefore, this paper aims to propose a new method to deal with a highly nonlinear behavior of MR damper using an extreme learning machine (ELM) method. The ELM method is applied to a meandering valve-based MR damper for damping force prediction, which has been recently developed. A simulation scheme is selected with damping force as the output, and current, velocity, and displacement as the inputs. The simulations are then carried out based on fatigue dynamic tests data in various frequencies and currents. The training times for more than nineteen thousand data points using the ELM method with 10, 100, 1000 hidden neuron numbers are less than 1.70 s, which is faster than the conventional ANN. Based on 50 times training processes, the ELM and ANN models have comparable average accuracies with R2 values of more than 0.95. ELM also has shown less value R2 standard deviation showing its advantage to reduce the possibility of being trapped in local solution compared to the conventional ANN

Frequency-dependent on the magnetorheological effect of magnetorheological plastomer

A low cross-linked magnetic polymer matrix also known as magnetorheological plastomer (MR plastomer) containing micron-sized carbonyl iron particles (CIPs) is a new kind of MR materials. MR plastomer can be prepared by two main methods which are physically and chemically crosslinking. However, the study on the dynamic properties of involving chemically crosslinked MR plastomer particularly the viscoelastic properties especially frequency-dependent are not systematically investigated. Therefore, in this study, the effect of the frequency on the MR effect and damping performance of chemically crosslinking MR plastomer under oscillatory modes condition were analysed. The magnetic particles namely CIPs were inserted into a plasticine-like polymer matrix causing the materials to exhibit an MR effect in response to an external magnetic field. Polymer base matrix was prepared using poly-vinyl alcohol (PVA), and boric acid (BA) was used as a cross-linking agent for chemically crosslinked MR plastomer. The MR plastomer samples were prepared using 70 wt% of CIPs as magnetic particles. The samples were tested using a rheometer with different test frequencies, which are 1, 5, and 10 Hz at the on-state condition. The experimental results revealed that the frequency has a significant correlation with the MR effect of samples where the MR effect of the sample decreased with the increment of test frequency. The MR effect for each sample at 1, 5, and 10 Hz are 6793, 5049, and 3131% respectively. In contrast, for the frequency sweep test, the storage modulus of the sample showed an increasing trend with the increment of test current, while the loss factor revealed an opposite result. The results proved that this kind of MR materials has the potential to be used in various of applications like soft actuator, vibration absorber, and force sensor