Miracle-dz/UniFAR-FTUs huggingface dataset:
A NLOS Detection Method Based on Kernel Principal Component Analysis

Radio signals propagating indoors will produce positive bias due to non-line-of-sight (NLOS) conditions, which seriously affects the accuracy of positioning. In order to distinguish between line-of-sight (LOS) and NLOS conditions, we combined the kernel principal component analysis method (KPCA), genetic algorithm (GA) and support vector machine (SVM) classification algorithm to propose a NLOS detection method based on KPCA-GA-SVM. A channel measurement campaign at frequency band from 2.4 GHz to 5.4 GHz was carried out in an indoor environment, and the proposed algorithm was evaluated through the measurement data. The results show that the KPCA-GA-SVM algorithm has a high accuracy for LOS and NLOS detection, and eliminates the correlation between different features, thus shortening the running speed of the algorithm.
Crossover level improvement between beams in a geodesic lens antenna based on a generalized Luneburg lens

In this paper, we propose a geodesic generalized Luneburg lens operating in the 60-GHz band, from 57 to 67 GHz, designed with the goal to improve the crossover level between beams with respect to previously reported fully metallic Luneburg lenses, while providing highly directive beams and wide scanning coverage. We have combined the use of a generalized Luneburg lens and the design of a compact feeding type based on ridge waveguide in order to achieve an optimum crossover level between beams keeping in mind manufacturing simplicity. The numerical evaluation of the final integrated lens design including 21 ridge waveguide feeding ports shows a |S11| parameter below −13.6 dB over the frequency band of interest, a scanning range of ± 52°, a realized gain higher than 19 dBi over the whole operating bandwidth, and a crossover level value between −3.5 dB and −4.2 dB from the lowest to the highest frequency of operation.
Multibeam Leaky-Wave Antenna for Mm-wave Wide-Angular-Range AoA Estimation

The design of a periodic leaky-wave antenna for angle-of-arrival (AoA) estimation is presented. Thanks to multiple radiating space harmonics, a multibeam operation is achieved in order to scan a large angular range with a limited fractional frequency bandwidth. The matching and radiation efficiency issues raised by the large spatial period involved with multibeam operation is addressed by using a dedicated unit-cell to control both the leakage constant and the Bloch impedance. Simulations confirm that a MUSIC processing enables the system to perform AoA estimation without ambiguity among the multiple beams. This allows for AoA estimation over the 180°-angular range with a bandwidth of only 3.7% at 27 GHz.
Substrate Integrated Waveguide Pedestal Filtenna for Sub-6 GHz 5G Radio Frequency Front-Ends

This paper presents the design, manufacturing, and measurement of a novel substrate integrated waveguide (SIW) pedestal filtering antenna solution for use in 5G New Radio sub-6 GHz communications. Miniaturization is achieved through the use of SIW pedestal resonators and integration of the radiating element into the filter design, resulting in higher order suppression. The design generates a third order filtering response utilizing SIW pedestal resonators and a patch antenna element. The manufactured SIW pedestal filtenna achieves a 10 dB return loss bandwidth of 4.29 % about a centre frequency of 3.63 GHz, and a boresight gain of 3.66 dBi. Additionally a 1×4 linear array is measured, demonstrating the applicability to 5G radio frequency front-ends.
Mechatronics Design and Kinematic Simulation of SCARA Robot to improve Safety and Time Processing of Covid-19 Rapid Test

Since the beginning of 2020, Peru has been exposed to the inevitable spread of Covid-19, a highly contagious disease that is increasing around the world rapidly. Scientists and researchers propose various solutions to mitigate its effects or reduce infections, from simple procedures to complex systems such as robots or vaccines. For this reason, innovative research was carried out from 2020 to 2021 under the supervision of the school of Mechatronics Engineering at U niversidad Tecnologica del Peru, so, this article, presents a SCARA robot prototype capable of performing rapid tests with blood samples to detect Covid-19. In addition, the robot has 4 servomotors and impactive end effector. In addition, this robot has 4 degrees of freedom, and this type was chosen for its speed and flexibility. This study develops a mechatronics conceptual design and kinematic analysis using the Denavit Hartenberg method using Matlab and, CAD in SolidWorks 2021. In conclusion, the purpose is to decrease the workload and protect the lives of people who perform the lab tests, being able to process them with greater precision.
Understand the influence of learning analytics dashboards on learner self-regulation and academic success

Since the beginning of the COVID-19 pandemic, many countries have adopted online education as an alternative to face-to-face courses. This has increased awareness of the importance of analyzing learning data left by students to improve and evaluate the learning process. This article presents a new tool, named TaBAT, created to work with different LMSs in the form of dashboards accessible online and allowing teachers to monitor the progress of their learners and at the same time and allow teachers to track the progress of their learners, while allowing learners to develop self-regulation skills and visualize their learning process. The results of a study conducted show that TaBAT helped learners to increase their progress and spend more time in the online course.
EXECUTE: Exploring Eye Tracking to Support E-learning

The outbreak of the COVID-19 pandemic has caused unprecedented disruption to education and progressed remote teaching as a predominant model for delivering educational content. However, the online teaching and learning model has its challenges, such as the lack of technological tools to quantity the student attention and engagement with the learning content. This paper focuses on developing an e-learning framework for capturing and analysing the students’ attention during remote teaching sessions and subsequently profiling their learning behaviour leveraging eye-tracking data. Our proposed eye-tracking solution deploys a webcam to capture and track raw gaze points that grant the user the freedom of natural head movement and scalability compared to conventional eye-tracking approaches. We derived various gaze metrics in conjunction with state-of the-art machine learning (ML) models like logistic regression, support vector machine and polynomial regression to classify the student attention with an accuracy above 91%. Furthermore, our findings can help in the early detection and diagnosis of attention deficit hyperactivity disorder (ADHD) among students, thus supporting their learning journeys by creating an adaptive learning environment tailored to their needs.
Dielectric Characterization and Chemical Concentration Sensing using T-Shaped Antenna

This paper presents a multifunctional, cost-effective, and easy to fabricate T-shaped patch antenna resonating in the frequency range of 18 - 25 GHz when evaluated without sample and resonant frequency in a range of 2.5 - 9 GHz when sample layer is introduced as superstrate in sensor structure, having a broad range of applications in the field of chemical and biomedical sensing. In the proposed design structure, the sensor layer is the topmost layer with a T-shaped patch antenna underneath with substrate and ground plane, respectively. Ethanol, Methanol, and edible fat solution samples are investigated for concentration sensing. The design demonstrated consistent behaviour of increase in resonant frequency by the value of at least 150 MHz with an increase in the concentration of Ethanol and methanol. In the case of edible fat, the resonant frequency increased by a relatively smaller value of 10 MHz with an increase in the concentration of edible fat in the sample layer.
A Sensor Using a Matryoshka Geometry Defected Ground Structure

A sensor using a Defected Ground Structure (DGS) based on the matryoshka geometry is described. The main idea is to take advantage of the miniaturization and selectivity characteristics of the matryoshka geometry. The proposed DGS sensor geometry is outlined, and initial design equations are presented. A prototype was fabricated and characterized. For the empty sensor, when comparing numerical and measured results, a very good agreement is achieved. Moreover, the resonant frequency determined using the proposed design equations represents a good agreement too. In order to verify the matryoshka DGS sensor sensibility, a mixture of distilled water and isopropyl alcohol was characterized and a calibration curve achieved. Finally, the matryoshka DGS sensor is compared with a dumbbell DGS sensor occupying the same area, confirming the miniaturization and selectivity characteristics of the matryoshka geometry. The achieved results and characteristics instigate the matryoshka DGS sensor research.
A 18.7 TOPS/W Mixed-Signal Spiking Neural Network Processor with 8-bit Synaptic Weight On-chip Learning that Operates in the Continuous-Time Domain

We present a mixed-signal spiking neural networks processor with 8-bit synaptic weight on-chip learning in 40 nm CMOS that consists of a 10k mixed-signal synapse circuit and 100 analog leaky integrate-and-fire (LIF) neuron circuits. The processor has no clock signal except in peripheral circuits for I/O, and neuron and synapse circuits can operate asynchronously in the continuous-time domain, just like biological neurons. We demonstrate the energy efficiency of 6.24–18.7 TOPS/W in a multitarget spike learning task.
Conceptual Specification of Forest Utility Balers for Wody Biomass

The new-concept biomass balers stem from recommendations of the forest engineering team at Humboldt State University to sort logging slash into poles that can be transported with conventional log trucks and into piles of finer tops and branches. While cost-effective collection and transport of small-diameter poles is well understood, handling and processing of branches and tops is problematic. Thus, the conventional forest management practice is to burn the piled fine woody biomass when weather conditions allow. Instead of burning the slash piles, the finer materials from slash including brush, tree tops and limbs can be ground into hog fuel on-site and transported in chip vans, or potentially can be baled by the envisioned new class of forestry balers for removal from the forest and processed into higher value products off-site.
A Deep Space Ka-band Antenna for CubeSat: Design and Multiphysics Analysis

This paper presents a stacked patch antenna optimized to operate over the down- and uplink Ka-band for Deep Space applications. The design of the antenna is performed with a numerical optimization, to control the bandwidth and the impedance matching. A bandwidth of 5.53@33.185 GHz has been obtained, with a gain around 8 dB. On this design, a multiphysics thermal and mechanical analysis is performed to verify the operational stability of the optimized antenna, mounted on a 1U CubeSat satellite. The analysis verified that the antenna temperature patterns can affect the antenna matching, but it still allows correct operation in the [−100° C,100° C] temperature range. Moreover, the stress analysis reports a level of mechanical deformation which does not compromise the use and functioning of the proposed stacked geometry in a Low Earth Orbit space mission.
Recent Advances in Artificial neural networks for EM parameterized modeling and optimization

This paper reviews the recent advances in artificial neural networks (ANN) for electromagnetic (EM) parameterized modeling and optimization. As an advanced ANN-based EM parameterized modeling and optimization technique, the neurotransfer function (neuro-TF) is discussed further in this paper. The trained neuro-TF parameterized models can be further used for EM design optimization with repetitive geometrical variations.
Microstrip Radiating Element with High Polarization Purity for Weather Observations

Next-generation weather radars are expected to be implemented with electronically-scanning planar phased arrays. Their scan agility could enable higher update rates and improved forecasts. However, application requirements on the polarization purity are hard to satisfy with a planar structure. Previous works in the literature have indicated a requirement of -22 [dB] on the Cross-Polarization Level (CPL) on scan under Ludwig's 2nd definition. This work presents a novel radiating element that approximates a magneto-electric dipole and has a printable planar structure. Preliminary numerical results show that the radiating element satisfies the CPL requirement in more than 98% of the scan directions of interest.
Experimental Demonstration of a Supergain Three-Dipole-End-Fire-Array

This paper presents the experimental validation of a three-dipole array optimized for maximum gain. The complex excitation coefficients associated to the array are calculated using a synthesis procedure based on the array factor. A parasitic array architecture has been selected to implement the proposed antenna solution. The high gain reached (8.6 dBi for a ka of 1.4) shows the interest of the proposed optimization method. Furthermore, it was shown that this optimization method decreases the sensitivity to errors in the dipole excitation coefficients at the cost of a lower directivity compared to the superdirectivity.
Analysis of Antenna Radiation Patterns by Means of Spherical Wavelets

A new tool for analysing antenna radiation patterns is proposed in this article. This tool consists in applying a spherical wavelet transform to the radiation which is assumed to be known on a regular spherical-coordinates grid. This is an alternative solution to usual plane-waves or spherical harmonic expansions. The interest of using wavelets is to obtain a better analysis in terms of localization in position and/or direction. The direct link between spherical wavelets and spherical harmonics is also highlighted. The properties of this tool are illustrated by means of numerical experiments performed on two typical antennas, a canonical aperture and a pyramidal horn.
Optimal Power Flow Approach for Cognitive and Reliable Operation of Distributed Generation as Smart Grid

Smart grid expertise emphasises on the compound connections of the electricity to the grid, along with computing, control and communication interface. It will bring together in future smart infrastructure for power system. Investigating these complex dynamic interactions is crucial for the efficiency and robustness of the emerging smart grid. In particular, it is one of the key elements for smart-grids to establish the dynamics among sources of grid. This paper proposes the vital operation of renewable energy sources (RES) like Solar Photovoltaic (PV), wind energy with existing grid of a. c. network of power system in view of cognitive reliable operation of RES as add-on source of power. The research presents sequence of operation of these sources by optimal power flow based on power flow chart for demand side management as a smart grid of power system. The system fulfils realistic operation for power system, based on fundamentals of power system, therefore a necessary research topology is developed, for well-regarded schemes of RES for setting up a pilot model so that demand side load should not be hampered and same is verified for linear and non-linear loads of electrical networks.
Wearable Harmonic Transponder for IoT Applications

The paper presents a novel compact wearable harmonic transponder, composed of a dual-band patch-type antenna loaded by an HSMS-2820 Schottky diode, operated at 869 MHz and 1 734 MHz frequencies. The antenna itself is optimized for a minimum ground plate size. The transponder performance was evaluated both in free space and when attached to a human muscle phantom. The total harmonic transponder conversion loss is 6.2 dB for an excitation power of -25 dBm at the antenna-diode interface, outperforming state-of-the art solutions. The measured reading distances are 9.0 m and 8.8 m for $\mathbf{EIRP}=20\mathbf{dBm}$ when the transponder is placed in free space and on the human body, respectively. As a means of machine-to-person communication, the transponder is suitable for Internet-of-Things concept.
Hyperbolic Positioning and Tracking of Moving UHF-RFID Tags by Exploiting Neural Networks

In this paper we propose a novel real-time tracking method of a moving UHF-RFID tag. Two antenna pairs fixed at predefined positions monitor the moving tag collecting phase measurements. Phase differences are calculated for each pair and then mapped to distance-differences of the two antennas from the target tag. The latter corresponds to a hyperbola for each pair of antennas. The intersection of the two hyperbolas denotes the position of the tag. Since solving the system of hyperbolas to find the intersection point is not feasible with standard practices, a neural network is exploited to approximate the solution. Compared to prior art, the proposed method does not require knowledge of the tag's initial position or the trace followed (e.g. conveyor belt). Experiments were conducted by placing a tag on a moving robot capable of performing SLAM (Simultaneous Localization and Mapping) to ensure knowledge of the ground truth. The results showed a mean error under 0.5m throughout the experimental campaign.
Self-Directed Learning using Eye-Tracking: A Comparison between Wearable Head-worn and Webcam-based Technologies

The COVID-19 pandemic has accelerated our transition to an online and self-directed learning environment. In an effort to design better e-learning materials, we investigated the effectiveness of collecting psychophysiological eye-tracking data from participants in response to visual stimuli. In particular, we focused on collecting fixation data since this is closely related to human attention. Current wearable devices allow the measurement of visual data unobtrusively and in real-time, leading to new applications in wearable technology. Despite their accuracy, head-mounted eye trackers are too expensive for deployment on large-scale deployment. Therefore, we developed a low-cost, webcam-based eye tracking solution and compared its performance with a commercial head-mounted eye tracker. Four-minute lecture slides on the 3rd year electronic engineering course were presented as stimuli to eight learners for data collection. Their eye movement was collected within the pre-defined area of interest (AOI). Our results demonstrate that a low-cost webcam-based eye-tracking solution, combined with machine learning algorithms, can achieve similar accuracy to the head-worn tracker. Based on these results, learners can use the eye tracker for attention guidance. Our work also demonstrates that these webcam-based eye trackers can be scaled up and used in large classrooms to provide real-time information to instructors regarding student attention and behaviour.
Off-grid Compressive Sensing Based Channel Estimation with Non-uniform Grid in Millimeter Wave MIMO System

Channel estimation is challenging for millimeter-wave (mmWave) communications because of the use of hybrid architecture and massive multiple-input multiple-output (MIMO) technology. By utilizing the sparsity in the angular domain, conventional on-grid compressive sensing methods can efficiently recover the channel state information (CSI). However, the channel estimation accuracy is severely affected by the off-grid errors and the selection of grid angles. The off-grid compressive sensing methods and the non-uniform grid angles can improve the channel estimation accuracy. In this paper, we investigate the impact of the non-uniform grid angles for the off-grid compressive sensing methods and the on-grid compressive sensing methods. We propose to employ the orthogonal matching pursuit (OMP) algorithm with interior point (IP) method based off-grid error mitigation to implement the channel estimation using the selected angle design. The simulation results demonstrate the advantages of the proposed off-grid compressive sensing method and show the impact of the non-uniform grid angles.
Cell Mechanics : Mechanical Properties and Membrane Rupture Criteria

Atherosclerosis is a chronic disease of the arteries that is a major cause of heart attacks and strokes. This thesis aims to provide novel insight into this disease by looking at specific factors involved in its development from a mechanical standpoint.Two important cell types involved in the development and progression of atherosclerosis are adherent endothelial cells and non-adherent leukocytes (white blood cells). We developed two devices that are able to measure the mechanical properties of both of these cell types. The first one, termed “profile microindentation”, uses micropipettes and microindenters to indent the cell, while the second one uses microfluidics to submit cells to an extensional stress.Further, we wondered if mechanics could help us understand when deformations undergone by cells, or stresses exerted on them, could become harmful.As a matter of fact, when atherosclerotic plaques occlude too much of the blood flow, the most common treatment consists of reopening the vessel with a balloon and keeping it open with a tubular wired mesh called a stent. This procedure exerts considerable compressive stress on the endothelium and is known to be associated with extensive endothelial damage. Hence, we seek to find a physical criterion that is predictive of endothelial cell membrane rupture under compression and to compare this to the stress exerted on the endothelium during the stenting procedure, to see if endothelial damage could potentially be avoided.Similarly, we seek to obtain a physical criterion that is predictive of leukocyte membrane rupture. We then compare and contrast the maximum possible deformations of leukocytes depending on whether those deformations are passive (such as when going through the microvasculature) or active (such as when leukocytes traverse the endothelial barrier).
Long‐term yields in annual and perennial bioenergy crops in the Midwestern United States

Many yield predictions in perennial bioenergy species have been made based on data collected during the establishment phase of growth or a limited number of long‐term studies. Few studies compare multiple perennial crops with the dominant agricultural vegetation of the landscape over long time periods. Here, we present the results of 11 years of perennial crop management on fertile agricultural soils in central Illinois, compared with conventional row crop maize/soybean (Zea mays L., Glycine max L.) production. We examined the long‐term productivity and drought susceptibility of Miscanthus x giganteus Greef et. Deu. ex. Hodkinson et Renvoize (miscanthus), Panicum virgatum L. , Cave‐in‐Rock cultivar (switchgrass), and a native prairie mix, in contrast to annual maize/soybean agriculture. Long‐term yields for miscanthus and switchgrass failed to reach initial predictions made during the establishment phase; however, in miscanthus, the 11th year of production shows little progressive yield loss with age, exceeding the modeled limit for the onset of age‐related decline. Harvest timing and differences in yields from hand and machine harvests in perennial crops likely contribute to overestimates of potential yields. Application of fertilizer to mature miscanthus resulted in significant increases in yield after a severe drought, though modeled effects of management and drought in miscanthus point to a more complex mechanism for yield response.