Miracle-dz/UniFAR-FTUs huggingface dataset:
Concept of Seamless Physical Observation of Human Hand Through Block Design Test

A seamless physical monitoring system for human observation is required to address the privacy and comfort concerns of the observed individuals. In this paper, we propose a human physical monitoring system integrated with a block design game-based approach to capture both human hand physical and cognitive capacities. By adopting this concept, we are able to naturally acquire human physical data. We have developed a smart block design, equipped with several sensors to track human activities from a physical standpoint. Supported by human skeleton and hand pose recognition, the data from Kohs blocks are processed in a human musculoskeletal simulation using MuJoCo, which comprises 59 hill-type muscles for each limb. To analyze performance for future studies, we conducted preliminary experiments involving humans playing the Kohs block design test. The results demonstrate that the proposed model effectively recognizes human hand muscle activity and muscle tension. Furthermore, the proposed concept is not only effective for assessing physical capabilities but can also be extended to cognitive capacities.
Monitoring Elbow Joint Recovery Rate in Rehabilitation

Rehabilitation is integral to achieving universal health coverage, complementing health promotion, disease prevention, treatment, and palliative care. Its core objective is empowering people of all ages to achieve maximum independence in daily life, including education, work, leisure, and family roles. Globally, around 2.4 billion people live with conditions needing rehabilitation. This demand is rising due to shifts in health profiles, with longer life spans but more chronic diseases and disabilities. Regrettably, rehabilitation remains largely unmet, especially in low- and middle-income countries, where over 50% of those in need lack access. Emergencies like conflicts and disasters create sudden spikes in rehabilitation needs, disrupting services. Rehabilitation is pivotal for Sustainable Development Goal 3, ensuring health and well-being for all ages. It encompasses various interventions like exercises, balance training, home modifications, and mobility aids, addressing impairments while considering individual and environmental factors.
IMAGEM DO BRASIL E ATITUDES DOS CONSUMIDORES SUL-AFRICANOSIMAGE AND ATTITUDES OF SOUTH AFRICAN CONSUMERS

This research aimed to analyze the effect that the image of Brazil has on the attitudes of South African consumers in relation to Brazilian clothing and shoes. For data collection, we conducted a field survey. The research sample consisted of inhabitants of the town of Stellenbosch, South Africa. For data analysis, two statistical techniques were used: Exploratory Factor Analysis and Multiple Regression Analysis. Based on the results, it was found that the country of origin effect that Brazil’s image has on South African consumers influence the attitudes of these consumers in cognitive level, in the footwear segment, and in the cognitive, affective and conative levels, in the clothing segment. This finding shows that, for the Brazilian fashion industry can effectively penetrate in South Africa, it is important there is a conscious and directed effort to form a positive image about Brazil among South African consumers.
A Comparative Study on Procedural and Predictive Systems on Drowsiness Detection

This study addresses the critical issue of drowsiness and driver fatigue, leading causes of fatal road accidents worldwide. The focus is on enhancing transportation safety by implementing an automatic driver drowsiness detection system using Python, incorporating OpenCV, Dlib, Tensorflow, and SciPy. The present study emphasizes the need for technological solutions to continuously monitor and measure driver attention, particularly during extended journeys. The proposed drowsiness detection system identifies closed eyes instances, alerting the driver to prevent potential accidents. A comparison between a deep learning LRCN model and a procedural EAR model shows that the EAR model achieves approximately three times faster execution time and higher accuracy in a GPU-exclusive environment. The procedural model achieves 83-86% accuracy on around 1000 images in 60-67 seconds, outperforming the LRCN model.
Machine Learning Techniques for Classification and Prediction of Structures and Rocks

Knowledge of the rocks, concrete, or constructions is often necessary. Mineshaft operations, salvage efforts, and tunneling are a few examples of such analyses. The real time analysis of the rocks and structures is one of the research areas where there are much things to explore and problems to solve. I propose a solution for the problem of real-time evaluation of the rocks and buildings based on machine learning algorithm. The structure and other properties of the stones, concretes, etc.…. are captured using sophisticated cameras and sensors. Support Vector Machine (SVM) is one of the most incredible AI procedures for performing grouping. Support Vector Network with outlier filters find and spots the outliers before the classification process. The proposed process classifies and predicts the type of rock under study with correctness precision of 85% in comparison with real characteristics of the rock.
Causal Inference and Causal Relationship Identification of Machine Learning in Econometrics

In econometrics, selecting variables, determining regression coefficients, and determining significance have always been a very important and challenging topic. However, machine learning technology has solved this challenge. This article aims to introduce machine learning and its related theoretical foundations, and explore its role in different application fields. Subsequently, experiments were conducted to analyze and diagnose the extracted feature parameters, and corresponding algorithms were proposed to verify the applicability and feasibility of the model. The results showed that the original algorithm had a prediction accuracy of 0.7, while the machine learning algorithm had a prediction accuracy of 0.9. Combining econometrics and machine learning methods can achieve predictive ability, providing decision-makers with more scientific and effective reference information to help make accurate choices. The commonality between machine learning and econometrics lies in the similarity of the data types and task forms used by the two. The final input for both types of methods is structured data, including dependent variables, independent variables, and case observations.
15843 - Respostas fisiológicas e comportamentais de bovinos a diferentes ofertas de sombra

Avaliou-se o efeito de diferentes disponibilidades de sombreamento na resposta fisiologica e comportamental dos bovinos em pastagens. Foram utilizados 12 3 vacas em lactacao num quadrado-latino (4x4), com os seguintes tratamentos: sem sombra, sombra unica, sombra em bosque e sombra dispersa. As variaveis foram: tempos pastando, ruminando e outros comportamentos; tempo na sombra e deitada; producao de leite; consumo de agua; temperatura retal e frequencia respiratoria. Os tratamentos bosque e dispersa nao apresentaram diferencas entre si e neles as vacas apresentaram maiores tempos pastando, ruminando, deitadas e na sombra, assim como, maior consumo de agua e menores temperaturas retais e frequencias respiratorias. Sem sombra as vacas apresentaram maiores tempos de outros comportamentos, menor consumo de agua e maior diferenca na frequencia respiratoria. Com sombra unica a ruminacao foi maior que sem sombra, se igualando aos demais tratamentos e as vacas apresentaram valores intermediarios, entre os tratamentos sem sombra e os demais, no tempo de outros comportamentos, no consumo de agua e na diferenca da frequencia respiratoria. Conclui-se entao que a ausencia de sombra na pastagem provoca estresse calorico; a sombra insuficiente ajuda a diminuir o estresse a um nivel intermediario e a distribuicao espacial da sombra nao apresenta diferenca significativa.
Landing UAV on Moving Surface Vehicle: Visual Tracking and Motion Prediction of Landing Deck

Landing an uncrewed aerial vehicle (UAV) on a moving surface vehicle in open waters is a formidable challenge due to the dynamic and complicated nature of the environment. The UAV is at risk of damage during the touch-down process, as the oscillatory motion of the surface vehicle can lead to hard impacts or tip-overs. In response, this paper introduces a trajectory optimization strategy for quadrotor for tracking moving surface vehicle using visual feedback from an onboard camera. The visual data is used for real time forecasting of the future motion of the surface vehicle. This prediction leverages a deep learning approach to determine the landing period in future when the surface vehicle's roll and pitch will fall below the critical landing threshold for a certain period, thereby providing a safe window for the impending landing. Our proposed method utilizes visual data from onboard camera on UAV only, obviating the need for external communication between UAV and surface vehicle. The proposed trajectory optimization method has been extensively tested in simulation environment and able to follow the target travel up to 5m/s even with highly maneuvering path. The proposed surface vehicle motion prediction was tested with recorded data and able to achieve the prediction error less than 3°, and in real-time manner.
Design and Analysis of High-Speed Voltage Level Shifter based on Wilson Current Mirror

This paper presents the level shifter’s multi-supply voltage design. Voltage level shifters serve as an interface between the controller and high-voltage or power devices, as opposed to the voltage the controller supplies. The numerous design blocks that operate at varying supply voltages are bridged using level shifters. The suggested method offers a voltage level shifter that uses Wilson current mirror level shifters (WCMLS) built on reflected output. The suggested method eliminates the limitation of traditional WCMLS by using a current mirror with an exceptionally high aspect ratio, hence saving a large amount of area. Utilizing Cadence Virtuoso™ and HSpice™, WCMLS is developed for 180 nm CMOS technology with a 1.8 model of MOSFET. According to the simulation results, the developed model can efficiently convert a low input voltage level of 3.3 V to an output voltage of about 6.5 V. Total power consumption, delay, duty cycle, rise time, fall time, and slew rate for the proposed level shifter are 43.57 W, 40.1 ps, 50.3%, 25.3 ps, 46.5 ps, and 205G, respectively.
UWB Band-Based Patch Antenna for Wireless Communication Systems

In this study, a patch antenna that operates in UWB band for Wireless Communication systems is envisioned and examined. The antenna is made to fit within small, portable devices, thanks to its low dielectric constant and substrate design with FR-4 (lossy). The antenna is simulated using CST Studio Suite, fabricated, and analyzed and its performance parameters, such as radiation pattern, gain, return loss, and bandwidth are evaluated. With Return loss of −22.84 dB, bandwidth of 510.7 MHz is obtained in Simulation, whereas with Return loss of −20.03 dB, a bandwidth of 641.8 MHz is obtained while tested with VNA. The radiation pattern is found to be omnidirectional, making it suitable for Communication systems applications. The intended antenna design is a low-cost and compact solution, and it can effortlessly integrate into various portable devices.
Latency Improvement Strategy for Temporally Stable Sequential 3DMM-based Face Expression Tracking

2D image-based face tracking is a core feature for multiple AR/VR applications. The latest advancements in self-supervised 3DMM face reconstruction maintained high-accuracy analysis-by-synthesis tracking but were not designed for online inference settings with low latency performance. Recently, state-of-the-art models such as MICA [1] has demonstrated significant improvement in term of accuracy for the offline face construction task but the design is ill-suited for practical use cases due to their long processing time on low and middle-end hardware. The original workflow includes two analysis-by-synthesis stages: face shape reconstruction and face tracking. The shape reconstruction aims to regress a neutral 3DMM model from the input. Then the tracking process learns relevant parameters for expressions, eyes, mouth, etc. for a differentiable render to reconstruct the original photographic input. This study aims to propose a design for an interface to apply offline 3DMM face tracking into an online inference pipeline for facial analysis-based applications.
Fiber- Reinforced Membrane Modeling Based on Constant Edge-Length Constraints

Fiber reinforcement is a well-studied approach for fabricating soft actuators. In this approach, an inextensible fiber is employed to constrain the deformation of a membrane in the fiber axial direction. In this way, the desired deformation can be achieved. In conventional finite element modeling, usually, the fiber and the membrane are modeled separately and then combined into a single model by imposing constraints. However, this is a time-consuming process. We propose a new approach to implicitly model an inextensible fiber by imposing geometric constraints, namely, the constant edge-length constraints. In our approach, the fiber model is integrated into the membrane model as mesh edges, and the two models are combined into one. As a result, the modeling process is simplified. The simulation results show that by employing the proposed model, the deformation behavior of fiber-reinforced soft structures can be simulated.
An Automatic Face Recognition Using Local Binary Pattern Histogram (LBPH) Algorithm

Face recognition is a rapidly advancing field with numerous applications in security, surveillance, biometrics, and human-computer interaction. This paper presents an innovative approach for automatic face recognition using the Local Binary Patterns Histograms (LBPH) algorithm. The LBPH algorithm is known for its simplicity, efficiency, and robustness in handling facial variations and occlusions. This research study conducts a comprehensive comparative analysis of the LBPH algorithm's performance against other popular face recognition methods, including Eigenfaces and Fisherfaces. It evaluates the algorithms on benchmark face databases and analyze their recognition accuracy, computational efficiency, and robustness to varying lighting conditions and facial expressions. The experimental results demonstrate that the LBPH-based algorithm achieves competitive performance, outperforming other methods in certain scenarios. Moreover, the algorithm discusses the strengths, limitations, and potential areas for improvement. The findings of this study contribute to the existing body of knowledge in face recognition and provide valuable insights for researchers and practitioners working in this domain.
Fractional-Order Control for Voltage Regulation in Bidirectional Converters: An Experimental Study

The theoretical application of fractional equations in controller development is not a new topic. The first efforts on this topic were reported in the late 1970s. However, in the last four years, a greater number of papers related to fractional control have been published than the one accumulated in previous years. Motivated by the above, this paper reports the step-by-step development of this type of control in a bidirectional converter. Furthermore, the discrete-time equivalent of the developed fractional control is implemented on Texas Instruments F280042C digital signal processor. The experimental results of the discrete fractional compensator are compared with the experimental results of a conventional proportional integral derivative (PID) controller. The results show a notable improvement in the response of the bidirectional converter with the fractional control; specifically, faster responses and less overshoot in most of the experiments carried out. Also, the existing challenges facing the widespread application of this control technique are notorious and are extensively addressed in this article.
Implementing Deep Learning Techniques for Trajectories of Flights to Yield Maneuver Status

The process of technological gentrification can be seen in various areas of aerospace engineering with the help of accumulated data. The collected data of aerial vehicles can be used for generating status of maneuvering that affects the trajectories of designated journey. Leveraging predictive analytics using deep learning can certainly automate the task of flight maneuvering. This research study implements various deep learning techniques to predict the maneuver status from the aerial vehicle accumulated data. The proposed research study is heavily result oriented and includes a complete process of network advancement to tackle every problem to reach the best possible metrics.
Real-time 3D Image-Presentation Micro-manipulation System with Force Feedback

In recent years, the demand for microinjections has increased manifold. The delivery of microinjections involves delicate manipulation based only on visual information, which requires a high amount of operational skill. To facilitate this process, we propose a 3D image-presentation micro-manipulation system with force feedback. By using a haptic device for manipulation, the proposed system allows the injection to be operated while receiving force feedback. We conducted manipulation experiments using porcine embryos to evaluate the effectiveness of the system.
Fail-Safe Controller for Personal Mobility Vehicles with BMI

As a hands-free device for personal mobility vehicles, PMVs, we have thus far presented a brain-machine-interface, BMI. The BMI, however, occasionally classified electroencephalography, EEG, signals into wrong control commands. As a result, a PMV exhibited inappropriate behavior and was in danger of collisions with curbs. In order for the PMV to avoid the collisions, we propose a fail-safe controller. For control commands from the BMI, the fail-safe controller determines the final control commands in consideration of surroundings. For this purpose, a depth map that estimates the depth values of an input image is simultaneously generated. The fail-safe controller is based on deep neural networks, such as convolutional autoencoder, CAE, and fully-connected layers. The CAE is used to derive depth maps, and final control commands are determined from feature maps extracted by the CAE through the fully-connected layers. For these multiple outputs, i.e., depth maps and final control commands, multi-task learning is applied to the fail-safe controller. The CAE is trained through deep convolutional generative adversarial network, DCGAN. The fully-connected layers are trained through supervised learning. Furthermore, we introduce undesired control commands that cause collisions into a dataset, especially when the PMV is close to curbs. Finally, we show the effectiveness of the proposed fail-safe controller for collision avoidance.
UAV Speed Control for Mobile Relay System to Ensure Transmission Quality and Throughput Stability

In this paper, we propose a novel mobile relaying method, where the relay nodes are mounted on unmanned aerial vehicles (UAVs). In the proposed method, each UAV is utilized as a moving relay to offer high performance for relay networks, especially for large and high-stability throughputs. Compared to conventional static relaying, our proposed method cannot only exploit the advantages of the dynamic relay system but also optimize the speed of each UAV to provide a new enhancement on network throughputs and the network's quality of services (QoSs). In addition, given a predetermined trajectory of each UAV relay, we define a novel solution for the optimization problem, which can both minimize throughput variation and optimize the speed of each UAV. Numerical results show that the proposed method can achieve significant throughput gains over the conventional relaying techniques.
Autonomous Navigation of a Mobile Robot with a Monocular Camera Using Deep Reinforcement Learning and Semantic Image Segmentation

Navigation systems for mobile robots using deep reinforcement learning have the potential to achieve autonomous movement without relying on precise environmental maps. This study focuses on autonomous navigation of mobile robots using monocular camera images as input through deep reinforcement learning. Typically, RL models are trained in simulation environments. However, there are gaps between simulation and real-world environments, making it challenging to apply trained RL models to actual robots. This challenge is particularly pronounced when using images as input. Semantic segmentation is employed to address this issue, as it can simplify complex RGB images into segmented images, thereby reducing the gaps between environments. In this study, a RL model to reach their destinations and a semantic segmentation model are acquired for mobile robot navigation. These models are applied to a ROS-based autonomous navigation system. Real-world experiments confirm the successful application of the learned models in actual environments.
Text-to-Image Generator using GANs

Text-to-image generation is a fascinating frontier in artificial intelligence, where machines translate textual descriptions into realistic visual representations. One remarkable approach that has stirred excitement and innovation in this field is the integration of Stable Diffusion, a ground-breaking deep learning framework. This fusion elevates the art of image synthesis, pushing the boundaries of what AI can achieve in creative content generation. At its core, text-to-image generation aims to bridge the semantic gap between language and vision, enabling machines to understand and generate images based on textual descriptions. Stable Diffusion, an evolution of generative adversarial networks (GANs), introduces stability and control to the training process. This method enhances the generation of high-quality images by mitigating issues like mode collapse, enabling better convergence, and facilitating the production of diverse and visually coherent outputs. The incorporation of Stable Diffusion into text-to-image generation projects has yielded remarkable results. It empowers AI models to create intricate and contextually relevant images from textual input. Whether it’s describing a serene mountain landscape or a whimsical unicorn in a magical forest, the AI harnesses the power of Stable Diffusion to breathe life into these imaginative concepts. One of the key advantages of Stable Diffusion is its ability to fine-tune the trade-off between image quality and diversity. By controlling the diffusion process, researchers and developers can manipulate the output variance, allowing them to strike the ideal balance for their specific application. This adaptability makes Stable Diffusion an invaluable tool in the toolkit of text-to-image generation projects. Moreover, the stable training dynamics offered by this framework empower developers to explore various text-conditional generation tasks beyond mere realism. It opens the door to generating images that evoke specific emotional responses, aligning AI-generated visuals with human intentions and artistic vision.