Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Optimization of the Blended Rolled Edge of a Rectangular Single Offset-Fed Compact Antenna Test Range Reflector Using Genetic Evolution

The physical and electrical size of the parabolic reflector largely determines the usable size of the Compact Antenna Test Range (CATR) quiet zone (QZ), with the reflector edge treatment being hugely influential in terms of determining the overall quality and uniformity of the collimated, pseudo plane wave. This is especially true at mm-wave frequencies where the edge illumination combined with the edge treatment can result in QZ ripple that quickly exceeds the widely used 1 dB and 10 degree peak-to-peak amplitude and phase ripple requirements. Recently, it has been demonstrated that modern powerful digital computational simulation technology in combination with evolutionary optimization strategies can be successfully harnessed to optimize the serrated edge treatment of an offset-fed CATR as part of the design process. This is crucial as it simultaneously attempts to maximize the performance of a given solution while ensuring the minimum use of space. In this paper, a further enhancement is presented in which the evolutionary computing technique is applied to the more geometrically complex blended rolled edge (BRE) treatment of a rectangular CATR reflector. Results of the validation of this novel technique are presented together with optimized QZ performance that highlight the successful use of this design procedure in the development of a new testbed for verifying base station system performance for a mm-wave, FR2, 5G New Radio applications.
Studying the impact of learning situation on learner model

To reduce the spread of COVID-19 pandemic, educational institutions were closed in all countries. This closure deteriorated the level of all learners and resulted in a considerable disturbance of the education system. In this context, distance learning was the best solution. This paper reports the findings obtained Likert scale survey on April 2020 sent to learners and teachers of the Tunisian universities. In this survey, the interviewees were asked about their opinions concerning e-learning in the new situation resulting from covid19 crisis. After describing the existing learner models, we present the findings provided by examining the impact of the learning situation on the learner model. Learners and teachers were asked to capture four profile dimensions: interaction, involvement, motivation and emotions during the COVID -19 health crisis. The analysis of the obtained results show that a new learning situation influences negatively the learner model, which proves the importance of considering the situational dimension in such a learner model.
An Integrated Microwave-Ultrasound Breast Imaging System: Initial Phantom Results

A novel, integrated microwave-ultrasound breast imaging system that requires minimal or no movement of the breast between scans has been developed. In this work, we present initial imaging results using a tissue-mimicking phantom, with experimental data obtained from the dual-mode system. The breast phantom is composed of four regions: skin, fat, fibrog-landular and tumour, each with complex-valued permittivity and acoustic speed designed to approximate those of real breast tissue. Quantitative 3-D ultrasound imaging is performed using a ray-based sound-speed reconstruction algorithm. Prior information is extracted from the sound-speed image and incorporated into the microwave finite-element method contrast-source inversion algorithm either as an initial guess or as a numerical inhomogeneous background. Although the resolution of the ultrasound imaging system itself is insufficient to detect the tumour, the low-resolution sound-speed reconstructions are used to improve tumour detection via microwave imaging. Resultant images with and without the ultrasound-derived prior information are presented. A simple thresholding technique is employed to assess the system's ability for tumour-detection. Preliminary findings are indicative of the potential of ultrasound-derived prior information in enhancing microwave imaging of the breast.
Omnidirectional Millimeter-Wave Propagation Characteristics of Corridor Environments based on Measurements at 28, 38, 71 and 82 GHz

This paper investigates measurement-based omni-directional millimeter-wave propagation characteristics for an indoor corridor environment. The omnidirectional characteristics were obtained with actual omnidirectional antenna measurements both at TX and RX, and not by synthesizing directional antenna measurements. With wideband sounding at 28, 38, 71 and 82 GHz, we analyzed path loss, shadow fading, excess loss and delay spread characteristics. In the path loss and the excess loss analyses, evident differences between the LOS and the NLOS were observed due to the waveguide effects. In the excess loss and the delay spread analyses, gaps were observed between the relative lower mmWave frequencies (28 and 38 GHz) and the upper frequencies (71 and 82 GHz).
A Novel RCS based CRFID Tag Design

In this paper, a Chipless RFID tag design based on Radar Cross Section (RCS) operating principle is proposed. The tag is implemented using a series of circular ring resonators to enable tag identification. The resonators are simulated and then fabricated on an FR4 substrate. A calibration technique using a ring resonator to improve the detection of 8-bit data is proposed and demonstrated. The fabricated tag is measured at a distance of 160 mm using the monostatic RCS method. The measurement results verify the simulation results and show accurate detection of the encoded information.
Accelerating Electromagnetic Inverse-Design using Deep Learning

One of the primary ways that deep learning has been applied to electro magnetics in recent years is for accelerating inverse design. We present one such method for designing metasurface supercells which are robust to structural erosion and dilation, a typical variety of nanofabrication error. A pair of deep neural networks are trained to high accuracy to predict diffraction efficiencies from a supercell mask, and then evaluated exhaustively to find tolerance bounds for freeform supercell designs.
Facial Expressions-Controlled Flight Game With Haptic Feedback for Stroke Rehabilitation: A Proof-of-Concept Study

Most stroke patients suffer from a combination of motor and sensory dysfunction and central facial paralysis. Specific rehabilitation training is required to restore those functions. Current research focuses on developing stimulating and straightforward rehabilitation training processes so that patients adhere to the training at home after hospital release. This study proposes enhancing patients’ enthusiasm to participate in facial muscle exercises and improving their postural perception and balance by controlling virtual objects to complete assigned tasks in virtual reality games using different facial expressions with the assistance of haptic feedback. The different rehabilitation exercises for motor, sensory, and facial dysfunctions were combined in one virtual reality game for the first time. The proposed haptic feedback device was modeled, simulated, and characterized. A user study was conducted to validate the proposed system. The experiment result shows that all the designed functions of the comprehensive stroke rehabilitation virtual reality game can be achieved. The added haptic feedback enhances the performance of the aircraft control with facial expressions by lowering the trajectory deviation by 22.57%. This implies that the proposed game may improve users' performance, thus attracting them to conduct more training.
Multipath-Enhanced Device-Free Localization using Low-Cost Ultra-Wideband Devices

Multipath propagation can improve device-free lo-calization (DFL). In this work, we therefore provide an overview of the required signal processing for multipath-enhanced device-free localization (MDFL) and introduce a possible sequential Bayesian approach. Furthermore, we demonstrate that low-cost ultra-wideband devices can be used for MDFL. Specifically, we use the Qorvo (DecaWave) DWM1000 module, for which we describe how to access the channel impulse response in detail. Based on a sparse network of ultra-wideband devices, we evaluate the localization performance of both DFL and MDFL for an indoor scenario. Thereby, MDFL is shown to clearly outperform DFL in terms of localization performance.
Array Antenna Fault Diagnosis Via Near Field Amplitude-Only Data

The paper deals with the diagnostics of realistic array antennas by exploiting phaseless measurements. Provided the number of faults is not small with respect to the overall number of elements, the proposed approach allows performing the diagnostics whatever the kind of radiating elements even if the measurement surface is located in the near-field zone. Despite the high non-linearity induced by the adoption of phaseless measurements, the underlying optimization problem can be cast as a Convex Programming one taking decisive advantage of the Compressive Sensing theory. Examples concerning 1-D array diagnostics are given for both cases of ON-OFF and phase faults.
Indoor Material Transmission Measurements between 2 GHz and 170 GHz for 6G Wireless Communication Systems

The sixth-generation wireless communication research activities were launched worldwide. A tendency of some researchers to use the sub-terahertz frequency band for 6G is noticed. This frequency band is selected as a candidate for 6G due to its remarkable wide unused frequency bandwidth. In this paper, typical indoor environment material transmission measurements from 2 GHz to 170 GHz are presented. These measurements aim to further understand and compare wave-material interaction for above and below 100 GHz frequencies by providing a continuous measurement up to 170 GHz of 16 different materials e.g. glass, plasterboard, concrete, and wooden materials. The measurement system is based on a vector network analyzer, with frequency extension modules for frequencies above 50 GHz.
A NUMERICAL MODEL TO PREDICT THE THERMODYNAMIC PERFORMANCE OF VARIABLE CAPACITY COMPRESSORS

The present paper considers the assessment of a numerical model to simulate the thermodynamic performance of variable capacity compressors (VCC), with especial attention to superheating. Each component is mathematically described, with an account for piston displacement as a function of crankshaft angle, thermodynamic processes inside the cylinder, valve dynamics and gas pulsation in mufflers. Several parameters are calculated for a compressor cycle, such as temperature, cooling capacity, isentropic and volumetric efficiencies. Thermodynamic properties for the refrigerant are evaluated through a program link to an external library. The compressor internal temperatures are supplied as input for the simulation program. This is accomplished by a routine that evaluates the temperature in eight control volumes through energy balances. The results show the model is limited to a range of compressor speed close to that adopted for its calibration condition.
Compact, Multiband, Flexible Decagon Ring Monopole Antenna for GSM/LTE/5G/WLAN Applications

The design of linearly polarized, flexible, multi-band decagon ring monopole antenna is proposed. The decagon ring monopole antenna incorporates a decagon ring having a thickness of 5 mm encircling a chamfered patch to generate multiband linear polarized waves. These radiators are excited by coplanar waveguide (CPW) fed based technique to induce two resonances at 2.20 GHz and 4.10 GHz covering bandwidth requirement of Global System for Mobile (GSM) 1800, Long Term Evolution (LTE) 2300/2600, Fifth Generation (5G) and WLAN bands. The simulated outcomes confirm the −10 dB impedance bandwidth of 48.84% (1.64 – 2.70 GHz) and 43.01% (3.65 – 5.65 GHz) at resonating frequency of 2.20 GHz and 4.10 GHz, respectively. Further, the bending analysis validates that the impedance matching and radiation performance of decagon ring antenna have minimal impact on the operating bands.
An Introduction to Dimensionality Reduction for Propagation Measurements

This paper presents the dimensionality reduction of a propagation channel dataset created through a measurement campaign. The scenario knowledge is essential for the signal processing prior to the transmission and the application of these techniques allows a quick scenario differentiation. The measurement campaign, which has been performed in the SWAT research group's facilities at the University of Granada, includes propagation channels from three different scenarios: anechoic, reverberation and indoor. t-SNE, a Machine Learning technique that performs a non-linear dimensionality reduction, has been applied to the propagation channels. The results show the separability of the three scenarios in a low-dimensional space for an optimal election of the t-SNE hyperparameters. The most relevant hyperparameters in the performance of the technique are the distance metric and the perplexity.
Analytical Form of the Scan Blindness Condition in an Infinite Phased Array of Printed Dipoles with a Superstrate

We use the exact form of the Green's function for a grounded dielectric slab with a superstrate to derive an approximate analytical expression for the propagation constant of the fundamental surface wave. The result is then used to obtain a closed-form formula for the scan blindness condition in an infinite phased array of printed dipoles with a superstrate. The analytical criterion of scan blindness is validated with a full-wave simulation of Maxwell's equations.
Optimization of optically powered sensors

The author has succeeded in achieving power supply for a distance of 1 km using a newly developed optically powered sensor system powered by pulsed light. This system employs the method of matching the impedances of load and of a photodiode by the optical/electrical conversion of pulsed light with a photodiode and boosting the voltage with a transformer. A high optical/electrical energy conversion efficiency of 14.8% was obtained from this system. By driving the semiconductor pressure sensor section with an optical input of 2.33 mW, sensing of a 1 km optical fiber cable length was realized. The accuracy of the prototype optical-power-supply-type pressure sensor was at a high level of +or-0.2%. A prototype of a microcomputer-mounted 'smart' detector was fabricated. <<ETX>>
Bandwidth Enhancement of Microstrip Antennas using Crossing Avoidance of Characteristic Modes

In this paper, a method of designing antennas by avoiding crossing between the modes is presented. This is aimed at enhancing antenna bandwidth using ground fragmentation techniques based on the characteristic modes analysis (CMA) approach. A compact and flexible Saxophone antenna and a conventional rectangular microstrip antenna both dimensioned at 30×30 mm2 (0.66λg × 0.66λg) are designed to operate at 5.8 GHz to validate the proposed method. The proposed saxophone structure with a full ground enhances the bandwidth up to 170 MHz (2.97%) relative to the conventional antenna. This is then improved up to 890 MHz (14.67%) and 794 MHz (13.58%) when integrated with a fragmented and a partial ground plane, respectively. In comparison, the conventional antenna exhibited a bandwidth of 360 MHz (6.38%) and 410 MHz (7.25%) for these cases. Modeling is performed using FEKO software based on two approaches: the method of moments (MoM) and CMA. Results show a satisfactory agreement of the predicted resonant frequencies from simulations and measurements.
Online assessment of computer vision and robotics skills based on a digital twin

This paper presents online simulation along with physical environments employing digital twins to develop robotic and computer vision applications, such as bin picking, by providing a virtual testing environment with real conditions for robotic systems and their surroundings. Particularly for situations such as the current COVID-19 pandemic, this approach permits the integration of knowledge and practice remotely without having the need of requiring a physical robot, increasing equity and inclusivity in the assessment, by reducing the gap between students from different institutions due to the lack of resources to buy the required robotic devices and even enabling remote connection to the classrooms. The proposed task can be performed as long as the educational center counts with basic computer equipment and internet connection. This paper presents a bin picker application based on mono and stereo vision to make the classification of cylinders of different colors and sizes and their arrangement in a base by using image processing and depth estimation algorithms. The performance of the classification was measured in terms of the error in millimeters from the difference of the desired and the obtained position. The images used for classification were taken in real life considering the environment conditions, and a digital twin of the bin picker and objects was made. The image classification and the overall instructions that the robot performed were generated on MATLAB, where the data from the real environment captured by cameras was sent. From here, the instructions were sent to URsim, which serves as the robot controller, and the final virtual simulation with the models of the base and cylinders was performed on Kuka-Sim Pro. This approach can then be utilized to implement different assessments based on personal results from simulation activities. As this assessment requires specialized knowledge regarding robotics, computer vision and classification algorithms, this methodology is intended to be applied for university students, at the level that is adequate according to the educational programs of each university.
Work-in-Progress: The Development of a Smart-Environments Learning Kit for Computer Science Classes

Embedding curriculum content in relevant contexts is a challenge computer science teachers face. The idea of smart environments as networking embedded computers to complete everyday tasks provides strong possibilities for context orientation, including smart homes, smart cities, and Industry 4.0. In particular, the context smart home offers teachers exciting and versatile possibilities to introduce students to computer science. It enables teaching programming, networks, communication protocols, and electrical engineering in a student-oriented manner. In order to provide teachers with a low-barrier opportunity to use this context in computer science classes, we developed a smart-environments learning kit. The kit contains hardware that can be easily interconnected and recombined, leaving plenty of room for individual development and internal differentiation. First pilot uses of the kit in different learning groups revealed room for improvement: For instance, preparing the boards in computer science classes is too time-consuming, and the open-source firmware of the hardware modules is too complex to be covered in class. Following a design-based research approach, this work-in-progress paper documents lessons learned from the piloting implementations and illustrates potential solutions.
Medicinal Plant Species Detection using Deep Learning

In the present era, the world is trending towards Automated Systems without human intervention with rapid development in Technology. One such area is medicinal plant species detection, where an expert identifies the medicinal leaf based on their botanical knowledge. We propose a deep learning model to detect the medicinal plant species based on its leaf image and advanced computer vision. This paper compares the Convolutional Neural Networks (CNN) variants viz., MobileNet, ResNet50, Inception v3, Xception, and DenseNet121 for Indian origin medicinal plant species detection. We have evaluated CNN variants to classify the medicinal leaf images and observed that the Inception v3 model outperforms all other conventional methods. Our proposed architecture adopts the Inception v3 model and the stochastic gradient descent technique during the training process for optimizing and achieving better results. Our experimental results show that the Inception v3 model achieved 95% accuracy in the Indian origin medicinal plant species classification
Simple Matching Technique using Finite Metasurface to Control Surface Waves

This article presents a simple matching technique to control surface waves (SWs) by using a finite metasurface matching section. The approach is based on a sub-wavelength array of patches which are employed as a simple and effective approach to control and guide SWs on a grounded dielectric slab (GDS). In order to excite the fundamental TM0SW mode, a horn-shape surface-wave launcher (SWL) in substrate integrated waveguide (SIW) technology was employed. The structure and the matching metasurface are modeled with an equivalent circuit. By using this matching section, the reflection and transmission coefficients are improved from −6 dB to −12 dB and from −9 dB to −3.5 dB at 23.7 GHz, respectively. In addition, thanks to the finite metasurface region, the generated TM0SW presents a uniform phase front allowing for efficient feeding of other antenna or passive elements implemented on the GDS. Measurements of a demonstrator prototype are also in agreement with full-wave simulations. This technique for SW control can be used in other microwave and millimeter-wave devices, for example, to reduce unwanted leakage, to route guided-waves, and feed leaky-wave antennas as well as other SW driven structures.
Assessment of command structuring in Scratch programming using the SOLO taxonomy

According to Wirth, “a program is algorithm plus data”. This association of algorithm and data also occurs at the micro-scale of a command, where the program is represented by a single command plus a data representation. This paper proposes to map the degrees of complexity of the command, data processing and data structuring to the levels of the SOLO taxonomy in order to create tool for evaluating code which is used in visual programming environments. This tool allows the teacher to assess the degree of the student’s understanding of the use of a command, and the complexity of that command. The ability of the teacher to measure the weight of each command, allows him to design alternative teaching spiral paths, from a simple command to the most complex ones. The work in this paper is part of a broader project to evaluate visual programming code – with Scratch as the chosen visual programming language.
A new species of Dermadena (Digenea: Lepocreadiidae) from the stone triggerfish Pseudobalistes naufragium (Tetraodontiformes: Balistidae) in the South American Pacific Ocean

Abstract The present paper describes a new species of Dermadena (Digenea) parasitizing Pseudobalistes naufragium in Puerto Pizarro, northern Peru, using light and scanning electronic microscopy (SEM). Additionally, molecular analysis was also performed to determine the phylogenetic affinities of Dermadena within the Lepocreadiidae. The new species is differentiated from Dermadena spatiosa, Dermadena stirlingi and Dermadena lactophrysi by presenting a curved and well-developed external seminal vesicle. Also, SEM revealed numerous dome-shaped tegument protuberances forming glandular papillae with transversal wrinkles arranged roughly in concentric rows around the acetabular region, varying in size from large at the middle of the body to small at the margin. In the molecular phylogeny, the new species formed a well-supported clade with sequences of species from the Lepocreadiidae, confirming that it belongs to this family.