Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor Testbed for Autonomous Localization Intelligent Control and Sensing

The ITALICS facility was developed to collect data using a suite of sensors in a flight-like environment without using an MRUAV. This testbed does not have the weight or flight time restrictions typically associated with an MRUAV. The facility is being used to evaluate how the choice of optical sensors can influence MRUAV system performance and testing effects of sensor fusion on navigation. To fuse the individual sensor data into a single model, sensor-to-sensor calibration is used to obtain the relative poses for multi-sensor configurations. Preliminary testing has been performed with the testbed to verify the individual sensor capabilities using localization techniques and the accuracy of the inter-sensor calibration.
Implementation of the Naïve Bayes Algorithm in Pest Prediction Systems

Indonesia is an agrarian country with a growing agricultural sector. However, the growth of agriculture is hindered by various factors, including attacks from Plant Pest Organisms, commonly known as pests. These pests pose a continuous threat to plants, leading to a decline in crop quality and significant impacts on society, such as economic and food security. The recurring pest attacks and inadequate pest control measures call for the development of an integrated pest control system. Previous pest control approaches have been reactive, solely based on past pest incidents, which lack preventive actions to anticipate future pest attacks. To address this issue, a pest prediction system is proposed as a preemptive measure to prevent crop failure caused by pest attacks. The prediction system employs Machine Learning techniques, specifically the Naïve Bayes algorithm, utilizing weather conditions such as temperature, humidity, and rainfall. The system's effectiveness is evaluated on cabbage crops, and the testing results indicate an accuracy rate of 89%, precision of 83%, recall of 83%, and specificity of 92%. These values are obtained by evaluating three tested pest species: Plutella xylostella, Spodoptera sp. , and Crocidolomia binotalis.
Penetration Depth Quantification of Open-ended Coaxial Probes for Dielectric Spectroscopy of Layered Media

Dielectric spectroscopy using open-ended coaxial probes is a powerful tool for biological tissue classification. It measures the complex permittivity of a medium as a function of frequency by applying an electromagnetic field and observing the energy reflected back. In heterogeneous tissue, a critical parameter that defines the accuracy of permittivity measurement is the penetration depth (PD) of the electromagnetic field for a given probe geometry, however, it is still unclear how the tissue characteristics affect the PD and the accuracy of the measurements. This paper evaluates the effect of various tissue and probe parameters on the PD in the context of dielectric spectroscopy through an open-ended coaxial probe. The PD is evaluated under different simulation conditions considering a probe inserted into a 2-layered tissue with different dielectric characteristics in 54,000 different simulations. A model for extracting the permittivity from the simulated reflection coefficient is also described. The results show for the first time that the PD increases as the difference in permittivity and conductivity between the layers increases, suggesting that measurement accuracy is sensitive to changes of contrast in the layer’s characteristics. The results also show that the PD decreases with the excitation frequency but increases with the diameter of the coaxial probe. These findings can greatly aid in quantifying and understanding the sensitivity of biological tissue classification using dielectric spectroscopy.
Evaluating the Benefits of Asymmetry Features for Emotion Recognition

Emotion recognition is a topic of interest in Affective Computing (AC). While deep learning architectures have gained popularity for classification tasks, their reliance on large datasets limits their applicability when data availability is scarce. An alternative approach is feature engineering, which involves extracting relevant features to train supervised machine learning models. Neuroscientific theories on emotion processing, such as the lateralization theory, have motivated the introduction of asymmetry features for emotion prediction. However, none of these studies have statistically evaluated whether including asymmetrical features could reduce classification error or computational time. To address that direction, the current work compared two approaches for emotion recognition. The first approach used features extracted from individual EEG channels, while the second used asymmetry features calculated by matching pairs of EEG nodes. The two approaches were compared in terms of performance and fitted computational time. The comparison indicated that the performances of both approaches were not statistically significant. Notably, the asymmetry approach required less computational time for the training stage. This finding implies that incorporating asymmetry features in emotion recognition models is viable when computational resources are limited, without significantly compromising performance.
On the Averaging of the Electromagnetic Field in Metasurface Modeling

In this work, we present a fully numerical modeling technique for 2D-periodic composite electromagnetic structures known as metasurfaces. Utilizing the field-flux eigenmode Finite Element formulation, we outline the treatment of the general eigenvalue problem pertaining to metasurfaces. Subsequently, we propose an averaging process of the associated electromagnetic fields at paths prescribed by the Generalized Sheet Transition Conditions. Average field components of a metasurface consisting of split-ring resonators are illustrated and discussed.
Active normal faulting, drainage patterns and sedimentation in southwestern Turkey

The geomorphology of the rapidly extending area of western Turkey is principally controlled by large active normal faults. The graben formed by the faults are asymmetric and about 10–20 km wide. Measurements of the rotation of Neogene sediments in the footwall of the active faults and 2–D modelling of gravity data show the extension across each graben to be about 6–10 km, suggesting that the whole region has undergone only small degrees of extension (β ≈ 1.2–1.3). The faults bounding the grabens are segmented on a length scale of 5–10 km with the segments linked by some means other than single ‘transfer’ faults. Where the footwall lithology is resistant to mechanical weathering, axial drainage is important and rivers cut through the line of the fault at the end of fault segments, producing large fans. Where the footwall lithology is less consolidated, drainage is generally in linear valleys. Major graben can have more than one phase of faulting, with the main graben bounding fault stepping successively into the hanging wall.
Light-weight Sketch Recognition with Knowledge Distillation

Recognizing hand-drawn sketches is a promising starting point for various applications, such as assisting artists in creating 3D environments for games or virtual environment scenes quickly and efficiently from concept arts. In addition, by understanding drawings, we can generate 3D models that can be used for further design and development. Thus, in this paper, we aim to develop a novel lightweight network that can accurately recognize sketch drawings. We propose a lightweight-yet-efficient neural network based on MobileNetV2 for sketch recognition and employ knowledge distillation to train the proposed model from EfficientNet-B4. To evaluate the accuracy of the proposed method, we collect a dataset of sketches comprising 1800 drawings in 12 categories, ranging from furniture to animals. The experimental results show that our network model achieves an accuracy of 96.7%, with 96.9% precision, 96.7% recall, and 96.7% F1-score. These results demonstrate that the proposed approach has great potential for practical sketch recognition applications, such as interior design or VR scene generation.
Radar-only Instantaneous Ego-motion Estimation Using Neural Networks

The problem of 2D instantaneous ego-motion estimation for vehicles equipped with automotive radars is studied. To leverage multi-dimensional radar point clouds and exploit point features automatically, without human engineering, a novel approach is proposed that transforms ego-motion estimation into a weighted least squares (wLSQ) problem using neural networks. Comparison with existing methods is done using a challenging real-world radar dataset. The comparison results show that the proposed method can achieve better performance in terms of estimation accuracy, long-term stability, and runtime performance compared to a representative approach selected from the recent literature.
Intention to Use Cryptocurrency Exchanges: Assessing Cryptocurrency Exchange Features

This study investigated the intention to use cryptocurrency exchange among cryptocurrency investors of the young generation in Jabodetabek. Adopting the Technology Acceptance Model (TAM), a theoretical model was acquired with three cryptocurrency exchange features were taken into account: security and privacy, fiat deposit and withdrawal, and user interface and user experience. Data collected from a purposive sample of 355 young cryptocurrency investors were computed and analyzed through SmartPLS with Structural Equation Modeling (SEM) method. The findings showed that the three cryptocurrency exchange features positively affect both perceived usefulness and perceived ease of use of cryptocurrency exchange, therefore contributing a positive substantial effect on the behavioral intention to use cryptocurrency exchange.
Influence of Fertilizer Levels and Mycorrhiza on Yield Attributes, Yield and Grain Quality of Hybrid Maize

A field experiment was conducted at Agricultural Research Station, Bhavanisagar during kharif 2009 to study the influence of mycorrhizal inoculation and fertilizer levels on the yield and grain quality of hybrid maize under irrigated condition. The experiment was laid out in a factorial randomized block design with four replications. Four fertilizer levels viz., 200:100:100, 150:100:100, 200:75:100 and 150:75:100 NPK kg ha -1 were the treatments under factor 'A'. Two mycorrhizal treatments viz., no inoculation of mycorrhiza (control) (M - ) and inoculation of mycorrhiza (M + ) were included under factor 'B'. The results revealed that application of 200:100:100 NPK kg ha -1 recorded higher yield attributes, yield and higher crude protein content. Among the mycorrhiza, mycorrhizal inoculated treatments recorded higher grain and stover yield. Regarding the treatment combinations, application of 150: 75:100 NPK kg ha -1 along with mycorrhizal inoculation recorded significantly higher yield parameters and yield. However, the yield was comparable with 200:75:100 NPK kg ha -1 along with mycorrhizal inoculation. Higher dose of NPK resulted in higher crude protein content but starch content showed a declining trend.
Modelling the relationship between the objective measures of car sickness

Car sickness is anticipated to occur more frequently in self-driving vehicles because of their design, especially the electronics and seating arrangements optimized for work and entertainment. Therefore, mitigating motion sickness is a crucial research area that is essential to the effective use of electronics in autonomous vehicles and, ultimately, their broad adoption. An investigation using machine learning techniques in combination with physiological measures (electrocardiogram, electrodermal activity and head movement) was done to detect and predict the severity of car sickness. A total of 40 adults (20 male and 20 female) were exposed to two 20-minute rides on a motion-base simulator, one while reading and one while performing no task. Car sickness incidence and severity were subjectively measured during the conditions using the Fast Motion Sickness Scale (FMS) questionnaire every two minutes and the Simulator Sickness Questionnaire (SSQ) at the beginning, midpoint and end of the experiment. Car sickness symptoms were successfully elicited in 31 participants (77.5%) while avoiding simulator sickness. Head movement had the strongest relationship with car sickness, and there was a moderate correlation between heart rate and skin conductance, and with a subset of participants, heart rate had a moderate correlation with car sickness. A classification score of 77% distinguishing between motion-sick and non-motion-sick participants was found using the random forest model. Overall, the findings suggest that physiological measures alone cannot be relied upon to reliably detect or predict the onset or severity of car sickness in real-time.
Miniaturized Single-Ended-to-Balanced Filtering Power Divider With High Selectivity Based on Tri-Mode Resonator

In this brief, a filtering power divider (FPD) based on a novel tri-mode resonator (TMR) is proposed firstly. Characteristic mode analysis (CMA) method is utilized to analyze the three modes of TMR in detail. Meanwhile, two transmission zeros (TZs) are generated due to TMR, which can improve the passband selectivity greatly. Then for the purpose of meeting the connecting requirement between single-ended and balanced components, a single-ended-to-balanced (SETB) FPD is realized on the basis of the FPD. In order to realize the miniaturization, a multilayer microstrip-slotline (M-S) transition structure is applied. As a result, the additional half-wavelength transmission slotline is not required. To validate the design method, the prototype has been manufactured and measured. The overall size is about $0.38\,\,\lambda _{\mathrm{ g}} \times 1.04\,\,\lambda _{\mathrm{ g}}$ and the common-mode (CM) suppression level is better than 28.5 dB at all frequencies (the fractional bandwidth is 200%). Furthermore, the in-band insertion loss is lower than 1.2 dB and the isolation is higher than 17.6 dB in the passband.
Silicon SAW parameters extraction and optimization using finite elements analysis

Saw (Surface Acoustic Wave Devices) are small, rugged, low cost, lightweight, reliable and extremely low power. A saw can be used as wirelessly interrogatable and completely passive device in hostile environments. It is important to use exact saw parameter measurement techniques and simulation methods that are able to model the saw devices accurately. In this paper, a simulation study of Rayleigh wave based on stacked Al/AlN/Si(100) devices was carried out. Evolution curves with respect to acoustic phase velocity, reflectivity and electromechanical coupling efficiency for different aluminum Al electrode patterns and different piezoelectric aluminum nitride AlN layer thickneses were quantified by 2D FEM COMSOL simulations. Simulated acoustic mode shapes are presented. The optimized AlN layers and electrode widths are the ones giving High values of coupling factor, velocities and reflectivity. They are deduced from the obtained dispersion curves.
Wideband Corrugated Horn Antenna for Cross-Polarization Jamming Applications at Ka-Band

In this paper, we report the design and realization of a corrugated horn antenna with high performances in terms of return loss, polarization purity, side-lobe levels and realized gain for use in cross-polarization jamming applications at Ka-band. The proposed antenna consists of a rectangular to circular waveguide transition, a mode converter where hybrid modes are stimulated and a Gaussian profiled part. The measurements show that the antenna has a good impedance matching (S11 ≤ -10 dB) at 28.2 – 40 GHz band and it operates (S11 ≤ -15 dB) between 30 GHz and 40 GHz. According to the numerical studies, its’ peak cross-polarization values in half power beamwidth is lower than -40 dB and 30 dB cross-polarization discrimination beam widths are higher than 7.7° at the whole operating band. Additionally, its’ average side-lobe level and realized gain are calculated as -40 dB and 30.1 dBi, respectively.
Continuous Human Activity Classification with Radar Point Clouds and Point Transformer Networks

Due to numerous benefits, radar is considered as an important sensor for human activity classification. The problem of classifying continuous sequences of activities of unconstrained duration has been studied in this work. To tackle this challenge, a radar data processing method utilizing point transformer networks has been proposed. The method has been experimentally verified on a dataset of human activities, and experiments have been performed to determine its optimal implementation. Promising preliminary results on a 9-class dataset show test accuracy and macro F-1 scores in the range of 83% and 73% respectively.
Authenticating Outsourced Location-Based Skyline Queries under Shortest Path Distance

An increasing number of location-based service providers are taking the advantage of cloud computing by outsourcing their Point of Interest (POI) datasets and query services to third-party cloud service providers (CSPs), which answer various location-based queries from users on their behalf. A critical security challenge is to ensure the integrity and completeness of any query result returned by CSPs. As an important type of queries, a location-based skyline query (LBSQ) asks for the POIs not dominated by any other POI with respect to a given query position, i.e., no POI is both closer to the query position and more preferable with respect to a given numeric attribute. While there have been several recent attempts on authenticating outsourced LBSQ, none of them support the shortest path distance that is preferable to the Euclidian distance in metropolitan areas. In this paper, we tackle this open challenge by introducing AuthSkySP, a novel scheme for authenticating outsourced LBSQ under the shortest path distance, which allows the user to verify the integrity and completeness of any LBSQ result returned by an untrusted CSP. We confirm the effectiveness and efficiency of our proposed solution via detailed experimental studies using both real and synthetic datasets
Creating an Accessible Future: Developing a Sign Language to Speech Translation Mobile Application with MediaPipe Hands Technology

The SignTech mobile application utilizes MediaPipe Hands technology to convert hand gestures of the American Sign Language (ASL) alphabet into text and subsequently into speech. Through MediaPipe Hands Solutions Framework, the developers mapped and calculated the hand landmarks to form each ASL letter. They compared the calculated hand landmarks to the landmarks defined for each ASL letter and used a matching algorithm to determine the best match. This innovative solution breaks down communication barriers, allowing deaf individuals to express themselves more easily and enabling hearing individuals to understand and engage with sign language users. The developers conducted a user study to evaluate the effectiveness and usability of SignTech, and the results demonstrate its success in translating sign language into speech. This research provides valuable insights into the development of technology for sign language translation and highlights the potential of MediaPipe Hands technology in improving communication between deaf and hearing individuals with a mobile application.
Zooplankton species diversity in the temporary wetland system of the Savannah River Site, South Carolina, USA

The Savannah River Site (SRS) near Aiken, South Carolina, USA, is home to the most diverse assemblage of zooplankton found in any temporary wetland system in the world. However, the diversity of this system has not been fully characterized. Here we present the results of a two-year biweekly survey of the zooplankton communities of 14 wetlands on SRS. Our goal was to examine patterns in αand βdiversity on both the landscape and local scales and link these patterns to environmental variation among and within these wetlands. We also examined how αand β-diversity change in these dynamic systems over time. Hydroperiod length proved to be an important factor that led to high species richness on the landscape scale, whereas distance, hydroperiod, and pH were related to community differences between wetlands. Both αand β-diversity varied considerably over time. High α-diversity was associated with the warmer months of the year when pH and conductivity were lower. Temporal changes in β-diversity suggest that similar species assemblages arise under similar environmental conditions annually. Wetlands that had a forested canopy generally had lower α-diversity than open, herbaceous wetlands; vegetated areas within a wetland had greater α-diversity, regardless of the vegetation type. These results build on previous work in this system to provide a better understanding of zooplankton diversity in the temporary wetlands of SRS, and should be broadly applicable to temporary wetland systems in the surrounding region.
Effect of Manual and Artificial Application of NPK Fertilizers on the Growth and Yield of Rhodes Grass (Chloris Gayana L. Kunth.) by Using Central Pivot Irrigation Technology

The present research work was conducted to find out the Rhodes grass response to manual and artificial application of NPK fertilizers by using central pivot irrigation technology in available agro-ecological conditions of Nooriabad, Sindh, Pakistan. In this study the quality and production parameters were studied for the safe keeping of the Rhodes grass. Green fodder in hay form is much more popular around the globe especially in UAE and Gulf Regions. And among all fodder grasses the Rhodes grass is becoming popular with the every passing day. It has high protein value (9-13%) and the average water consumption for Rhodes Grass production is about 600 mm to 1200 mm. As the Rhodes Grass crop gives the yield for multiple years therefore, extra ordinary care of farming operations are required. Arid regions where summer comes early the Rhodes Grass sowing can be done from March to April and August to September. One time sowing of Rhodes grass will give the production to the growers for more than three years.
Evolution of Kotlin Apps in terms of Energy Consumption: An Exploratory Study

Context. Java and Kotlin are the two main programming languages used to create Android applications. Kotlin almost completely replicates the capabilities offered by Java and offers extra features, making it a popular choice among developers. From a sustainability perspective, it is crucial to assess the energy usage of Kotlin-based Android applications. Goal. The goal of this study is to explore how the energy consumption of Kotlin applications evolves over time. The study also aims at identifying the key factors that influence energy consumption, to inform developers on how the changes they make affect the energy consumed by their applications. Method. To investigate how Kotlin apps' energy consumption changes through releases, we study three open-source apps Kotlin apps that are also present in the Google Play store. We conduct a measurement-based experiment during which we assess the energy consumed by several releases of each studied application, for a total of 171 executions. Afterwards, we statistically analyse the collected data to identify relevant energy fluctuations (i.e.,, spikes, drops). Finally, we manually inspect the source code changes in the apps to identify possible causes of the identified energy fluctuations. Results. All three studied applications exhibit a growing trend for energy consumption over the course of their releases. Moreover, abnormal energy spikes are found for all applications. There are different causes behind these variations, including OS upgrades, new features, poorly chosen design patterns and libraries, UI issues, and unstable app versions. Conclusions. Our study provides evidence that a number of not fully understood factors can affect the energy consumption of a mobile application. Further work is needed to study their impact.