Miracle-dz/UniFAR-FTUs huggingface dataset:
Beam Position Design for Low-latency LEO Satellite Communications with Beam Hopping

Since the latency of LEO satellite communications with beam hopping is important and mainly determined by the number of beam positions (BPs), we investigate the BP design problem, aiming at minimizing the number of BPs subject to a predefined requirement on the radius of BP. A low-complexity user density-based BP design (LCUD-BPD) scheme is proposed, where the original problem is decomposed into two subproblems, with the first one to find the sparsest user and the second one to determine the corresponding best BP. In particular, for the second subproblem, a user selection and smallest BP radius (USSBR) algorithm is proposed, where the nearby users are sequentially selected until the constraint of the given BP radius is no longer satisfied. These two subproblems are iteratively solved until all the users are selected. Simulation results have shown that the proposed scheme can substantially reduce the complexity with little performance sacrifice compared to the existing methods.
Swarm Robot SLAM with Point-Line Matching in Weak Information Environments

Simultaneous Localization and Mapping (SLAM) in weak information environment scenarios where there are only sparse and ambiguous references are available presents significant challenges. This paper introduces a swarm robots based point-line matching based solution to enhance the accuracy and robustness of localization and mapping within such environments. Our approach employs a centralized serverbased architecture for the swarm robot system and utilizes an ORB-based Distributed Bag-of-Words (DBoW) algorithm to construct point and line feature dictionaries. By generating point-line pairs, the proposed method effectively addresses perceptual overlap issues, thereby improving the overall performance of localization and mapping for swarm robots. Experimental results validate the superiority of the proposed approach in comparison to traditional methods, highlighting its potential for real-world applications in challenging environments for swarm robot SLAM.
Orthodontic approach to gemination: a clinical case report

: Background : Dental gemination is an abnormality of size and shape that occurs when a dental germ attempts to divide in two by invagination, resulting in the incomplete formation of two teeth. This dental anomaly causes discrepancies in the arch and interdigitation, producing deviation of the midline, crowding, late eruption of other teeth, some form of malocclusion in the dentition, among others. Case Report : 16-year-old female patient with a geminated lower right incisor, asymptomatic that requires multidisciplinary treatment to achieve functional and aesthetic harmony of her teeth. Discussion: The approach for this clinical case was more conservative than previous studies by opting forFinkelstein´s second option, accompanied by a root canal treatment and interproximal enamel reduction (IPR). A posterior restorative treatment on the tooth with gemination is suggested to improve the aesthetics of the tooth and cover the exposed dentin tissue. Results : The geminated tooth was integrated into the arch with an ideal anatomy, the shape of the arches are more uniform, and the dental crowding was released.A functional occlusionmolarand canineclass I were obtained and by incorporating the geminated tooth with an ideal anatomy avoiding dental extractions. Conclusion: There are few registered cases carried with orthodontics, despite this the orthodontist must be able to identify the anomaly and by knowing the options to solve the problems that may be present to be able to plan an effective treatment
On the Impact and Lessons Learned from Mindfulness Practice in a Real-World Software Company

Background. Mindfulness is a meditation technique whose main goal is educating attention by focusing only on one thing at a time, usually breathing. Mindfulness practices improve concentration and attention, being particularly valuable in demanding and high-stress work settings, such as those found in software companies. A family of five controlled experiments on the impact of mindfulness on future and current software engineers' performance has been carried out in six years, whose participants practiced mindfulness daily for several weeks. Aims. This work has a twofold purpose, to present the fifth experiment in the series and to summarize the lessons learned across the family of experiments. The fifth experiment was carried out at INPRO, a public software company in Seville (Spain), in order to evaluate whether software workers improve their performance and some psychological factors, i.e. attention awareness, techno-stress and well-being, compared to a control group. Method. Employees of two departments (Development and Operation) were recruited to participate in the study. Mindfulness (the treatment) was applied to 24 subjects who attended mindfulness sessions daily for six weeks, while the other 27 subjects were the control group. For all subjects, such psychological factors were measured using questionnaires, whereas performance was measured using INPRO's task management systems. Results. Findings have shown significant differences in terms of attention awareness and techno-stress levels between the practitioners who practised mindfulness and those who did not. Benefits on the perceived well-being have also been reported by participants after the continued practise of mindfulness. Regarding the performance, the analysis depicts inconclusive results, probably due to the small size of the sample, a problem which was accentuated by significant variability in the kinds of tasks performed in both departments. Conclusions. Mindfulness practice has yielded significant benefits in the series of experiments, such as performance in the academy and psychological factors in the industry. Nevertheless, its impact on performance in software companies requires further research, since the limited data availability on subjects' performance has led to a small sample size, ultimately posing challenges in drawing dependable conclusions.
BiocircuitR: An Innovative Adaptive Circuit Training for Outpatients with Cardiovascular Disease. Methodological Approach

Background. Physical activity (PA) is associated with benefit in both primary and secondary cardiovascular prevention. A balanced exercise program which incorporates both resistance and aerobic exercise is the foundation to developing a long-lasting wellness lifestyle. AI and machine learning can be performed for different usages in fitness such as training sessions monitoring and personalized training plan. The aim of the project is to evaluate the efficacy of Biocircuit in the functional capacity and in the prescription of physical activity in subjects with cardiovascular diseases. Methods. All subjects refer to an extra-hospital rehabilitation clinic (ABCardio, Bologna). The functional capacity of the subjects will be evaluated through the following tests: Maximal cardiopulmonary exercise test (CPET), 1-km Moderate Treadmill-Walking Test (1-k TWT), Six-minute walking test (6MWT), One-repetition maximum (1-RM) and exercise program to improve cardiovascular endurance and muscle strength. All exercises will be performed through Biocircuit (Technogym, Cesena) a circuit training fully automated and based on the “Biodrive” electromechanical motor built on aerospace technology. The collected data are transmitted to the private cloud-based for data storage and background processing. PA levels and lifestyle evaluation will take place through the administration of questionnaires: the 7-day physical activity recall questionnaire (PAR), SF-12 questionnaire and the evaluation of METhour/week. Expected Results. Increase and maintenance of usual weekly physical activity and validation of a new algorithm for the estimation of VO2peak on Biocircuit instruments are the two main results that the project intends to achieve. Conclusion. Biocircuit applied to secondary prevention can provide a contribution to the formulation and monitoring of personalized programs in cardiac patients.
Close-Distance Optical Flow Fusion Using EKF for Multirotor UAV Position Estimation

This paper proposes the use of close-distance optical flow measurements to enhance the Extended Kalman Filter (EKF) position and velocity estimation accuracy of Unmanned Aerial Vehicle (UAV) with the use of existing infrastructure in a GPS-denied environment. The scenario assumed in this project involves a UAV navigating through a mine shaft with a stationary central steel cable that passes through the shaft's center. The UAV has a special design with a hole in the center that allows it to fly up and down while keeping the central cable in its geometric center. The UAV is equipped with a camera facing parallel to the cable at a close and constant distance. Using optical flow algorithms, we can estimate the vertical velocity of the UAV. Using the EKF filter, we aim to integrate these velocity measurements with existing measurements from the Inertial Measurement Unit (IMU), magnetometer, and barometer, to improve the accuracy of the UAV's vertical velocity and height measurements.
Utilizing Cloud Services for Advanced E-Health Applications, Enhancing Diagnostics and Treatment Through Vertex AI and Vision API

Cloud computing services offer a wide range of capabilities that can significantly enhance applications in the healthcare domain, particularly in the areas of diagnostics and treatment. This research paper explores the utilization of specific cloud services, such as Vertex AI and Vision API, to expand E-Health systems. The paper explores how Vision API can be utilized to enable intelligent image analysis and interpretation for various medical imaging approaches it also proposes a potential system design on how healthcare providers can utilize it. The paper also examines how Vertex AI, with its machine learning capabilities, can improve diagnostic accuracy and treatment planning through advanced algorithms and predictive models. The research discusses the benefits, challenges, and ethical considerations of employing these unique cloud services in digital health, paving the way for more accurate diagnoses, personalized treatment plans, and improved patient outcomes.
A Tailorable Robotic Hand Orthosis to Support Children with Neurological Hand Impairments: a Case Study in a Child's Home

Neurological disorders such as traumatic brain injuries (TBI) can lead to hand impairments in children, negatively impacting their quality of life. Fully wearable robotic hand orthoses (RHO) have been proposed to actively support children and promote the use of the impaired limb in daily life. Here we report a case study on the feasibility of using the pediatric RHO PEXO for assistance at home in a 13- year-old child with hand impairment after TBI. The size and functionalities of the RHO were first fully tailored to the child's needs. We trained the child and their parent on independently using the RHO before taking it home for a period of two weeks. The use of the RHO improved hand ability. Additionally, the tailoring and training benefited the unimanual capacity (Box and Block Test score +2 after tailoring) and bimanual performance (Assisting Hand Assessment score +4) of the child with PEXO. Further, it increased device acceptance by the child and the parent. The child used PEXO at home for 76 minutes distributed over three days during eating and drinking tasks. Personal and environmental factors caused the moderate use. No adverse events or safety-related issues occurred. This study highlights the value of tailoring an assistive RHO and, for the first time, demonstrates the feasibility of home use of a pediatric RHO by children with neurological hand impairments.
Towards Higher Current and Voltage LCLs

LCLs are widely used devices for power control and distribution in satellites. Traditionally, P-type MOSFETs have been used due to their simplicity from the control perspective. Actual ESA standard defines LCLs up to class 10 (10A) and 50V. However, 100V bus voltage is common in high power platforms and the current trend is to increase even more this value, around 300V. In this new scenario, the classic concept of LCL design needs to be revised, and this work proposes a simple alternative for P-type MOSFETs that operates at high voltage and can be easily scaled up in current.
Integrating Finite-Element Model of Probe Element in GPU Accelerated Ultrasound Image Simulation

Simulations are able to evaluate the impact of many parameters at reduced cost and time. In the frame of imaging systems development, their accuracy is remaining a concern. On one hand, finite planar baffle model allows image simulation but is not able to model complex phenomena occurring inside transducer’s layers. On the other hand, finite-element method (FEM) models layers interactions but is too heavy to simulate images. In this paper, realistic 3D images are simulated using an FEM transducer element model and an approximated sound propagation model. The Angular impulse responses (AIR) concept is introduced to describe the radiation performance of axis-symmetric acoustic sources. Echo radio-frequency signals are computed in frequency domain using a graphical processor unit (GPU). Far field approximation and electro-acoustical reciprocity are assumed. The electrical environment is modeled and the element receive sensitivity is estimated using a FEM simulation. Imaging a phantom with several millions scatterers and fully developed speckle is performed in four hours only.
An Efficient 1D Autoencoder-Based Approach for R-Peaks Detection in Electrocardiogram Signals

Electrocardiogram (ECG) signal which is composition of multiple segments such as P-wave, QRS complex and T-wave plays a crucial role in evaluating human heart cardiac diagnosis. For an analysis of cardiac diagnosis, it is required that clinicians scan the ECG signal for R-peaks (the highest peak of the QRS complex) detection, which relies on their expertise and takes enormous time. In order to provide more realistic treatment of cardiovascular diseases in the ECG signal with noises and different characteristics, computer-based R-peaks detection approaches have been actively developed in the research article for many years and generally compose of two major operations; preprocessing and decision. Contrary to previous approaches which require high computational costs and complexity, appropriate initial parameters, a novel CNN based approach is presented for R-peaks detection in this paper. Both qualitative and quantitative analysis are performed in the real ECG signal taken from typical MIT-BIH arrhythmia database. In order to validate the success of the recommended approach, total 18 different R-peaks detection approaches reported in the literature are compared by utilizing performance evaluation metrics. Average values of sensitivity, positive predictivity, error rate, accuracy and f-score are computed as 99,96%, 99,95%, 0,09%, 99,90% and 99,95% respectively, which are the best values among well-established studies for R-peaks detection. It is demonstrated with qualitative and quantitative results that better performance is achieved by our suggested approach.
VIBES: Vibro-Inertial Bionic Enhancement System in a Prosthetic Socket

The use of vibrotactile feedback is of growing interest in the field of prosthetics, but few devices fully integrate this technology in the prosthesis to transmit high-frequency contact information (such as surface roughness and first contact) arising from the interaction of the prosthetic device with external items. This study describes a wearable vibrotactile system for high-frequency tactile information embedded in the prosthetic socket. The device consists of two compact planar vibrotactile actuators in direct contact with the user's skin to transmit tactile cues. These stimuli are directly related to the acceleration profiles recorded with two IMUS placed on the distal phalanx of a soft under-actuated robotic prosthesis (Soft-Hand Pro). We characterized the system from a psychophysical point of view with fifteen able-bodied participants by computing participants' Just Noticeable Difference (JND) related to the discrimination of vibrotactile cues delivered on the index finger, which are associated with the exploration of different sandpapers. Moreover, we performed a pilot experiment with one SoftHand Pro prosthesis user by designing a task, i.e. Active Texture Identification, to investigate if our feedback could enhance users' roughness discrimination. Results indicate that the device can effectively convey contact and texture cues, which users can readily detect and distinguish.
Impedance Control of a 2-DOF Spherical 5-Bar Exoskeleton for Physical Human-Robot Interaction During Rehabilitation and Assessment

This paper presents a novel impedance controller for THINGER (THumb INdividuating Grasp Exercise Robot), a 2-degree-of-freedom (DOF) spherical 5-bar exoskeleton designed to augment FINGER (Finger INdividuating Grasp Exercise Robot). Many rehabilitation and assessment tasks, for which THINGER is designed, are improved by rendering near-zero impedance during physical human-robot interaction (pHRI). To achieve this goal, the presented impedance controller includes several novel features. First, a reference trajectory is omitted, allowing free movements. Second, force-feedback gains are reduced near actuator limits and a saturation function limits the maximum commanded force; both allow more responsive (higher) force-feedback gains within the workspace and mitigate transient oscillations caused by external disturbances. Finally, manipulability-based directional force-feedback gains help improve rendered impedance isotropy. Validation experiments included free exploration of the workspace, following a prescribed circular thumb motion, and intentional exposure to external disturbances. The experimental results show that the presented impedance controller significantly reduces impedance to subject-initiated motion and accurately renders the desired isotropic low-impedance environment.
Cotton Leaf Disease Identification Using Transfer Learning

Accurate identification and classification of plant diseases play a vital role in safeguarding global food production and ensuring the economic well-being of the stakeholders. Various solutions have been developed using Deep Learning (DL) based systems for different essential crops. However, the research landscape regarding intelligent solutions for automatically classifying cotton leaf diseases remains largely unexplored despite cotton being a significant commercial crop in many regions of the world. This article describes a method for detecting diseases in cotton leaves based on transfer learning. Unlike the previous works focusing on a limited number of classes and/or samples, we combined samples from different resources and curated a dataset of seven cotton leaf diseases and one healthy class. The dataset reflects the challenges of heterogeneous sources, varying backgrounds, illumination, inter-class similarity, etc., making it suitable for estimating performance in real-life scenarios. After that, we assessed the effectiveness of various pretrained Deep Convolution Neural Networks, including InceptionV3, ResNet152V2, VGG16, InceptionResNetV2, Xception, MobileNetV2, and DenseNet121, where the VGG16 model achieved the highest accuracy of 95.02%. Finally, we provided a thorough class-wise performance and error analysis to show the capability of the model under different scenarios.
Compiler Auto-Tuning via Critical Flag Selection

Widely used compilers like GCC usually have hundreds of optimizations controlled by optimization flags, which can be enabled or disabled during compilation to improve the runtime performance of a compiled program. Due to the large number of optimization flags and their combination, it is difficult for compiler users to tune compiler optimization flags manually. In the literature, many auto-tuning techniques have been proposed, which find a desired setting on all optimization flags (i.e., an optimization sequence) by designing different search strategies in the entire optimization space. Due to the huge search space, these techniques suffer from the widely-recognized efficiency problem. To reduce the search space, in this paper, we propose a critical-flag selection based approach CFSCA which first finds flags potentially relevant to the target program by analyzing program structure and compiler documentation, and then identifies critical flags through statistical analysis on the program's predicted runtime performance with various optimization sequences. With the reduced search space, CFSCA selects a desired optimization sequence. To evaluate the performance of the proposed approach CFSCA, we conduct an extensive experimental study on the latest version of the compiler GCC with a widely used benchmark cBench. The experimental results show that CFSCA significantly outperforms the four compared techniques, including the state-of-art technique BOCA.
Two-Dimensional Visualization of Intense Ultrasound Field using Confined Particle Movement Caused by Radiation Pressure Field

This paper presents an easy-to-use visualization apparatus for high intensity midair ultrasound field. In many applications of such high-power ultrasound that have been devised so far, construction of minutely designed ultrasound field is crucially important for their performance to the degree of practical use. However, adjustment of generated ultrasound fields has been not an easy task because ultrasound is invisible, and visualization of such fields usually requires scanning of microphones. My proposed method just utilizes passive mechanical component that consists of lightweight polystyrene beads confined between two layers of acoustically translucent membranes. This paper describes how ultrasound fields are visualized with my method and gives theoretical insights for the observed phenomenon.
Soil organic carbon and soil respiration in deserts: examples from the Kalahari

Thomas, A. , Hoon, S. R. , Mairs, H. , Dougill, A. J. (2012). Soil Organic Carbon and Soil Respiration in Deserts: Examples from the Kalahari. In L. Mol, & T. Sternberg (Eds.), Changing Deserts: Integrating People and their Environment. (pp. 40-60). The White Horse Press.
Double Direction Minimum Variance 2D Array Beamformation for 3D Photoacoustic Imaging

Minimum variance (MV) beamformer has been commonly used to improve lateral resolution of photoacoustic imaging. Recently, photoacoustic imaging with 2D array transducer has been increasingly used for 3D imaging; therefore, 2D array beamforming algorithms are required to improve resolution in multiple axes. 2D MV beamformer can improve lateral and elevational resolution simultaneously but suffers high computational complexity which is O(N6). In this article, we proposed a simplified version of 2D MV – double direction MV (DDMV), applying MV on lateral and elevational direction while requiring less computational complexity, which is O(N4). Our simulation results show that, compared with delay and sum beamformation (DAS), DDMV can improve both lateral and elevational resolution by 48.8% and 84.9% respectively, and has similar resolution improvement as 2D MV.