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In this article, an ultra-wideband antenna has been presented for medical applications which have a resonant frequency of 10.35GHz and offers a bandwidth of 1400MHz with a return loss of -19db. The presented antenna is low-cost lightweight and can easily be integrated inside the circuit. As this antenna is designed for medical applications its size is compact and is fed utilizing a coaxial feeding technique which is especially plentiful for operative radiotherapy applications. The designed antenna is rectangular and covers an overall size of 24x12mm with a thickness of 1mm. The proposed antenna is designed using CST studio as a simulation tool, the extracted results of important parameters like return loss, surface current, reference impedance, and far-field have achieved remarkable results which are illustrated in the article. which makes it suitable for radiotherapy. A high epsilon valued εr = 4.8, tan δ = 0.02 substrate GT-008 has been employed as a dielectric material whereas copper is being used for the ground and radiating patches. The performance of the antenna in the X-band is satisfactory which makes it suitable for radiotherapy.

This paper presents the concept of impedance diversity to mitigate the disturbance caused by varying impedances of non-linear loads in the time domain. Simulations were performed in LTSpice to show the concept of the impedance diversity, and measurements were performed using a speed-controlled water pump as the source of interference and PLC modems as the victim. The impact of the interference from the water pump on the PLC modem is quantified with the Frame Error Rate (FER), which is calculated as the ratio between the erroneous frames and total frames sent by the PLC. The measurement result shows that the impedance mode of the PLC modem affects the frame errors caused by the interference from the water pump.

Conducted electromagnetic interference cases are increasing due to the rapid increase of energy efficient electrical equipment in smart grids. For example static energy meter readings are interfered due to non-linear pulsed currents drawn by dimmed lighting technology and a speed-controlled water pump. A communication interface can be added to the static energy meter to form a smart meter, for example using power line communication which works in the CENELEC-A band. This paper shows that the interference from a water pump does not only interfere with the readings of static energy meters but also affects the power line communication of a smart meter. It is shown that the interfered and lost frames during communication are affected by the average impedance of the speed-controlled water pump.

Indoor positioning is useful in various venues including warehouses, convention centers, malls, airports, nursing homes. In these scenarios, reducing the complexity of location estimation both improves responsiveness and helps to elongate battery life of the mobile device. In this work, we carry out a detailed analysis of the impact of Principal Component Analysis (PCA) on the computational complexity and accuracy with different machine learning algorithms on a large data set containing 520 APs. We compare the algorithms' training and testing times, as well as their accuracies in the presence and absence of PCA. Our results show that (i) PCA significantly reduces both the training and testing times for classification and regression using k-nearest neighbor (kNN) and support vector machine (SVM) algorithms while preserving if not improving accuracy; (ii) PCA slightly improves the training/testing times for regression using multi-layer perceptron (MLP); (iii) random forest (RF) does not perform well with PCA.

This article presents a new control scheme for an aircraft that has suffered from actuator failure due to severe damage to its vertical tail. The damaged degree of the vertical tail is treated as uncertainty. Firstly an output feedback controller is designed for the nominal lateral dynamic model of aircraft. However, when the actuator's fault/failure occurs, the idea of generalized virtual actuators is adopted. In this way, the same baseline controller is used even in the presence of fault/failure. Linear matrix inequality is used to compute the controller and virtual actuator's gains. The simulation results show good tracking performance and fault-tolerant competency.

Modifying the surface of tubes to improve heat transfer is a popular practice all over the world. Grooves, dimples, protrusions, and corrugations are being used to improve the inside and outside surfaces of tubes and channels. Different channels are being used in the electronics such as the channels in the microprocessor for heat exchange. These channels are differentiated based on their shapes and coolants used in them. These heat channels minimize the losses that occurred due to the heating up of devices. This research presents a numerical study and comparative analysis of heat transfer and flows characteristics with different pipe geometries with the addition of nanoparticles. A simulation-based software is used for changing the geometry of channels and concentration of nanoparticles in flowing fluid to make a comparative analysis of straight and dimple-protrusion overlapped technology. The rate of heat transfer is used to make this distinction. The nanoparticles of Copper oxide with a concentration of 2% and 4% are added in fluid and a maximum increment of 15% is obtained in Nusselt Number.

Wi-Fi fingerprinting has been widely used for indoor positioning, as Wi-Fi technology is easily deployed and supported. In fingerprinting, a database is created using the received signal strength indicator (RSSI) values in the area of interest; position prediction is performed by finding the best match for a measured RSSI among the values in the database. As location positioning gains importance for continuous interactive (CI) applications in large indoor spaces such as malls and airports, the fingerprinting databases become larger, making it computationally more difficult to position targets in real-time. On the other hand, CI applications such as Augmented Reality (AR) require low-latency positioning for a good user experience. In this work, we propose to use feature selection methods along with the K-nearest neighbors (KNN) classification and regression algorithms in order to create a simple and swift location positioning system. Our evaluation of various feature selection methods shows that computation times for positioning can be reduced by 75% using feature selection.

This paper presents the impact of LED lamps on the performance of Narrowband Power Line Communication (NB-PLC) in the CENELEC-A band between 9-95 kHz. NB-PLC in is only one of the many victims of interference in the frequency range 2-150 kHz. Only few emission standards for this frequency range are available, while the number of interference cases is growing rapidly. Most equipment will fail at a specific frequency and/or level. PLC is chosen as a victim, as the performance of the PLC is rated through well-established parameters. One of these is the Frame Error Rate (FER), which is calculated as the ratio between the erroneous frames and total received frames. The number of LED lamps has a strong correlation with the peak amplitude of the current pulses from the LED lamps and the FER of the PLC data frames.

We design a 4EO-PnC (4 sides extended octagonal shape phononic crystal) of MEMS (Microelectromechanical system) materials to control the acoustic waves in MEMS devices such as resonators and biosensors. An ultra-wide acoustic bandgap is detected in one-dimensional(1D) (BG ratio 121.8% to 135.84%), and two-dimensional (2D) (BG ratio 116.04% to 130.71%) ‘in-plane mode’ of MEMS materials through FEA (Finite Element Analysis) method. We analyze that the bandgap of 4EO- diamond PnC is the extremist one. The bandgap of one dimensional (1D) and two-dimensional (2D) ‘out-of-plane’ mode for 4EO- diamond PnC are also explore. Additionally, for the declaration of wide bandgap in 4EO-PnC it is observed that very wide peak attenuation is successfully lie in the same frequency region where the band gaps are detected.

Automatic segmentation of retinal vessels tree is a prerequisite step in the field of ophthalmology for computer-aided diagnosis. This paper describes a robust vessel segmentation algorithm that is efficient enough to segment retinal vessels tree automatically and accurately. The proposed method includes several steps. Initially, we splitted the color fundus image into three channels Red, Green and Blue channels. On Green channel, CLAHE is used for contrast enhancement and top-hat morphological operation are applied to remove noise. Meanwhile High Boost and Frangi filters are introduced to get the prominent vessels pixels. Finally, the ISODATA thresholding technique is applied to get final binarized output segmented image. The proposed method outperforms recently published methods in terms of statistical parameters, such as Accuracy, Sensitivity, and Specificity on DRIVE and STARE datasets. In addition, the proposed method showed a remarkable scientific value for future computer-aided diagnosis applications.

The new approach of silicon based phononic crystal with drilling across its two sides is proposed to control the acoustic waves. A very large band gap is obtained by one dimensional 1D and two dimensional 2D drilled phononic crystal in desired frequency ranges. When these novel phononic crystals is employed to proposed centered hole thin film piezoelectric disc resonator the anchor loss reduced and quality factor $Q$ improved from 92100 to 127 billion at operating frequency 178.07MHz, and low vibrational displacement is obtained based on Finite Element Analysis simulation. It is observed that this novel PnC improve the quality of resonator with better performance.

In digital images, blur wraps significant information and makes automatic image analysis a challenging task for computer vision algorithms. Hence, accurate blur detection and classification becomes essential to understand the information wrapped up in blurry images. In this paper, we proposed a novel automatic blur and non-blur region detection, and classification technique “Frequency-based Multi-level Fusion Transformation” (FMFT) to detect the unwanted blurry region and classify the blur and non-blur regions using single image processing. Our proposed approach mainly works with frequency sub-bands in patch wise manner, without having any prior information regarding camera configuration, type of blur and intensity of blur. Moreover, the detected Tri-Map from FMFT is further processed to perform blur and non-blur regions classification along with sharp object detection from blurry images using KNN-Matting. The average F1-score of 0.98 signifies the effectiveness of the proposed method in terms of blur detection and classification. Additionally, the proposed method also outperforms existing state-of-the-art techniques.

Many digital images contain blurred regions which are caused by motion or defocus. The defocus blur reduces the contrast and sharpness detail of the image. Automatic blur detection and segmentation is an important and challenging task in the field of Computer vision “e.g. object recognition and scene interpretation” that requires the extraction and processing of large amounts of data from sharp areas of the image. Therefore, the sharp and blur areas must be segmented separately to assure that the information is extracted from the sharp regions. The existing techniques on blur detection and segmentation have taken a lot of effort and time to design metric maps of local clarity. Furthermore, these methods have various limitations “i.e. low accuracy rate in noisy images, detecting blurred smooth and sharp smooth regions, and high execution cost”. Therefore, there is a dire necessity to propose a method for the detection and segmentation of defocus blur robust to the aforementioned limitations. In this paper, we present a novel defocus blur detection and segmentation algorithm, “Local Triplicate Co-occurrence Patterns” (LTCoP) for the separation of in-focus and out-of-focus regions. It is observed that the fusion of extracted higher and lower patterns of LTCoP produces far better results than the others. To test the effectiveness of our algorithm, the proposed method is compared with several state-of-the-art techniques over a large number of sample images. The experimental results show that the proposed technique obtains comparative results with state-of-the-art methods and offers a significant high-speed advantage over them. Therefore, we argue that the proposed method can reliably be used for defocus blur detection and segmentation in high-density images.

The growing consensus is that creativity is associated with connectivity across multiple neural networks including, but not restricted to, networks involved in executive and cognitive control along with regions closely linked to the Default Mode Network (DMN) of the brain. In this study, the effects of a nine-week creativity training given to managers with a minimum of five years of experience in their field was explored. Power Spectral Density computed from scalp electrical signals and P300 waves derived from ERP techniques paired with Kaufman Domains of Creativity Scale (K-DOCS) were used. After the training, an increase in "Everyday" creativity was observed, implying that this aspect of creativity is possibly more dependent on the processing of the DMN than on attention and executive function.

With the exponential increase in the Energy demand and high reliance on thermal generation, Pakistan is having severe energy security issues. Harvesting of renewable energy for the achievement of green sustainable energy is the way forward. Pakistan is blessed with a strategic location having highly optimum solar irradiation throughout the year. Solar PV modules are installed to harvest the energy. With the changing sun trajectory along the year, to improve the harvesting, PV systems are equipped with solar tracker that improves the energy yield. Quaid-e-Azam Solar Power Park in Bahawalpur is a landmark project by Government of Pakistan to set the orientation towards renewable energy. The solar arrays in the power park are set at a fixed angle of 32°. This study takes into consideration the results obtained in the yield by the alteration of tilt angles using the single-axis and dual-axis tracking systems. The simulations are carried out on the PV SYST and RETScreen software to indicate the optimization potential and its translation to reduction in GHG emissions. The results obtained indicates an improvement of 16.71% and 22.40% respectively in the annual yield of the Quaid-e-Azam solar power using single and dual axis tracker respectively. In addition to this, the optimization tends to the further reduce the GHG emissions by 17.8% and 24.65% employing single and dual axis tracking.

The faults diagnosis of machines consists of feature extraction and classification of faults. The fault diagnosis process is based on the fact that each fault in a machine has a unique vibrational feature. Every industry wants a compact device or embedded system of low cost for the early faults diagnosis of machinery. This paper presents the platform that is focused at the embedded system design for the early faults diagnosis of machine(s) and classification of faults. We performed our experiment on the test rig apparatus and collected the vibration signals of four states of machine, those were: normal state, cracking state, offset pulley state and wear state. In segmentation, we use the empirical mode decomposition (EMD) technique. For classification purpose, we are using k-nearest neighbors (K-NN). Achievement of this research is that embedded system design for the classification of different faults in the machines. The overall accuracy of our experiment is 91.5%.