Miracle-dz/UniFAR-FTUs huggingface dataset:
On the role of regularity in mathematical control theory

In this thesis, we develop a coherent framework for studying time-varying vector fields of different regularity classes and their flows. This setting has the benefit of unifying all classes of regularity. In particular, it includes the real analytic regularity and provides us with tools and techniques for studying holomorphic extensions of real analytic vector fields. We show that under suitable “integrability” conditions, a time-varying real analytic vector field on a manifold can be extended to a timevarying holomorphic vector field on a neighbourhood of that manifold. Moreover, in this setting, the “nonlinear” differential equation governing the flow of a timevarying vector field can be considered as a “linear” differential equation on an infinite dimensional locally convex vector space. We show that, in the real analytic case, the “integrability” of the time-varying vector field ensures convergence of the sequence of Picard iterations for this linear differential equation, giving us a series representation for the flow of a time-varying real analytic vector field. Using the framework we develop in this thesis, we study a parametizationindependent model in control theory called tautological control system. In the tautological control system setting, instead of defining a control system as a parametrized family of vector fields on a manifold, it is considered as a subpresheaf of the sheaf of vector fields on that manifold. This removes the explicit dependence of the systems
Fractional Differential Mask Based Retinal Image Enhancement–A New Approach

Retinal image is one of the robust and accurate biometrics mainly used in diagnosing diseases such as diabetes or hypertension. Retinal images often have low grey level contrast and dynamic range due to the acquisition process that can seriously affect the diagnosis results. In this paper, we propose the use of Grunwald-Letnikov (G-L) fractional differential operator for effective retinal image enhancement. In the proposed algorithm, the fractional differential mask is convolved with the retinal image in vertical and horizontal directions for the given fractional order. The performance of the proposed method is improved by changing the size and fractional order of the mask. The main objective of using fractional differentials for image enhancement is to enhance non-linearly the low-frequency components, which is often zero on application of integer-order differentiation. The fractional differentials have the ability to give more textural information from an image and are robust to noise, when compared to integer-order differential. The retinal images for experimental analysis have been chosen from standard databases namely High-Resolution Fundus (HRF) Image database (18 image pairs), DIARETDB0 (130 images) and DIARETDB1 (89 images) database. The results of the proposed method are compared with the traditional enhancement methods like Histogram Equalization and Adaptive Histogram Equalization. Experimental results show that the proposed method for image enhancement outperforms the existing ones, in terms of entropy and average gradient. This technique can be successfully used for a number of image processing applications. The G-L fractional differential mask based enhanced images might be helpful for easier identification of Optic disc.
Modelling Magnetoresistance Effect in Limited Anisotropic Semiconductors

A macroscopic model of the magnetoresistance effect in limited anisotropic semiconductors is built. This model allows us to solve the problem of measurement of physical magnetoresistance components of crystals and films. Based on a unified mathematical model the method is worked out enabling us to measure tensor components of the specific electrical resistance and the relative magnetoresistance of anisotropic semiconductors simultaneously.
Determining the level of superstition belief in pregnancy

Objective: This study was conducted to evaluate the superstitions of pregnant women. Method: This descriptive and correlational study was conducted with 278 pregnant women living in Agri between October 2021 and August 2022. Results: It was determined that the total mean score of the pregnant women on the Superstitious Belief Scale was 36.16±13.75. A statistically significant difference was found between the educational status, monthly income, perception of pregnancy and superstition levels of the pregnant women [p<0.05]. Conclusion: It was found that the total Superstitious Belief Scale scores of the pregnant women were moderate and unplanned pregnancy, income and education level affected the superstition score means.
An Examination of Teacher Understanding of Project Based Science as a Result of Participating in an Extended Professional Development Program: Implications for Implementation.

Project-based science (PBS) aligns with national standards that assert children should learn science by actively engaging in the practices of science. Understanding and implementing PBS requires a shift in teaching practices away from one that covers primarily content to one that prompts children to conduct investigations. A common challenge to PBS implementation is a misunderstanding of the elements of PBS. Identification of these misunderstandings as well as implementation challenges could inform professional development. This case study examined 24 teachers’ understanding and implementation of PBS during participation in a consecutive three-year, comprehensive professional development program. Results provide insight as to the process they followed in the transition to implementing PBS. Measures included classroom observations, reflective interviews, and attitudinal surveys. Results showed that teachers developed the knowledge, confidence, and understanding to implement PBS but in most cases it took at least two to three years for positive results to become evident. Teachers struggled to develop adequate driving questions that provided projectfocused lessons. Other obstacles included teacher resistance to student-directed instruction, confusing inquiry-based instruction with hands-on activities, and inability to motivate students to work in collaborative teams. While challenging, over time the teachers developed the knowledge, desire, and skills to implement PBS.
L10-MnGa based magnetic tunnel junction for high magnetic field sensor

We report on the investigation of the magnetic tunnel junction structure designed for high magnetic field sensors with a perpendicularly magnetized L10-MnGa reference layer and an in-plane magnetized Fe sensing layer. A large linear tunneling magnetoresistance ratio up to 27.4% and huge dynamic range up to 5600 Oe have been observed at 300 K, with a low nonlinearity of 0.23% in the optimized magnetic tunnel junction (MTJ). The field response of tunneling magnetoresistance is discussed to explain the field sensing properties in the dynamic range. These results indicate that L10-MnGa based orthogonal MTJ is a promising candidate for a high performance magnetic field sensor with a large dynamic range, high endurance and low power consumption.
Integrity monitoring of vehicle positioning in urban environment using RTK-GNSS, IMU and speedometer

Continuous and trustworthy positioning is a critical capability for advanced driver assistance systems (ADAS). To achieve continuous positioning, methods such as global navigation satellite systems real-time kinematic (RTK), Doppler-based positioning, and positioning using low-cost inertial measurement unit (IMU) with car speedometer data are combined in this study. To ensure reliable positioning, the system should have integrity monitoring above a certain level, such as 99%. Achieving this level when combining different types of measurements that have different characteristics and different types of errors is a challenge. In this study, a novel integrity monitoring approach is presented for the proposed integrated system. A threat model of the measurements of the system components is discussed, which includes both the nominal performance and possible fault modes. A new protection level is presented to bound the maximum directional position error. The proposed approach was evaluated through a kinematic test in an urban area in Japan with a focus on horizontal positioning. Test results show that by integrating RTK, Doppler with IMU/speedometer, 100% positioning availability was achieved. The integrity monitoring availability was assessed and found to meet the target value where the position errors were bounded by the protection level, which was also less than an alert level, indicating the effectiveness of the proposed approach.
Humanoid Loco-Manipulations Using Combined Fast Dense 3D Tracking and SLAM With Wide-Angle Depth-Images

—To efficiently achieve complex humanoid loco-manipulation tasks in industrial contexts, we propose a combined vision-based tracker-localization interplay integrated as part of a task-space whole-body optimization control. To achieve good perception complementarity between manipulation and localization, a new fast dense 3D model-based tracking using wide-angle depth image is developed and used in conjunction with a simultaneous localization and mapping software. Our approach allows humanoid robots, targeted for industrial manufacturing, to manipulate and assemble large-scale objects while walking. It is assessed with experiments consisting in rolling and assembling in an unwinder a heavy and wide bobbin using bimanual grasping and bipedal locomotion at a time. This experimental use-case is found in some large-scale manufacturing where bobbins are enrolled with various materials (cables, papers, rubbers, etc.). The same experiments are made using two different humanoid robots of the same family.
Study on parameters of fiber loop mirrors as artificial saturable absorbers

This work focuses on practical analysis of the Nonlinear Optical Loop Mirror (NOLM). This all-fiber device serves as a saturable absorbers for passive mode-locking in ultrashort pulse generation. It has a great potential for further development as it’s made in all fiber technology, and can be easily adapted to custom system. We investigate and provide experimental results as well as characteristics of NOLM’s in several different setups. The all-fiber laser system used for testing produced femtosecond pulses with a positive chirp. We present reflection as a function of input power and as a function of the coupler’s coupling. Also, relevant spectral characteristics are shown. These results may be useful in further development of fiber laser systems and other applications.
Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges

This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, including wheeled tractors, tracked tractors, rice transplanters, high clearance sprays, agricultural robots, agricultural tractor–trailers, etc. The application and improvement of path-tracking control methods are summarized based on the different working scenes and types of agricultural machinery. This study explores each of these methods in terms of accuracy, stability, robustness, and disadvantages/advantages. The main challenges in the field of agricultural vehicle path tracking control are defined, and future research directions are offered based on critical reviews. This review aims to provide a reference for determining which controllers to use in path-tracking control development for an autonomous agricultural vehicle.
Speaker Verification Based on Single Channel Speech Separation

In multi-speaker scenarios, speech processing tasks like speaker identification and speech recognition are susceptible to noise and overlapped voices. As the overlapped voices are a complicated mixture of signals, a target extraction method from this mixture is a good front-end solution for further processing like understanding and classifying. The quality of speech separation can be assessed by the noise ratio or subjective scoring and can also be assessed by accuracy of the downstream processing tasks like speaker identification. In order to make the separation model and speaker identification model more adapted to complex multi-speaker speech overlapping scenarios, this research investigates the speech separation model and incorporate with a voiceprint recognition task. This paper proposes a feature-scale single channel speech separation network connected to a back-end speaker verification network with MFCCT features, so the accuracy of speaker identification indicates the quality of speech separation task. The datasets are prepared by synthesizing Voxceleb1 data, and used for training and testing. The results show that using an objective downstream evaluation can effectively improve the overall performance, as the optimized speech separation model significantly reduced the error rate of speaker verification.
Krylov Subspace Solvers in OP2 on GPUs

An extensible framework for the execution of Krylov Subspace solvers is presented. The framework implements the Conjugate Gradient method using OP2 and CUSPARSE & CUBLAS libraries. Timings are taken for OP2, CUSPARSE & CUBLAS and serial versions of the implemented Conjugate Gradient method and OP2 is found to be sensitive to the structure of the input matrix for the linear system being solved. A metric called the ‘structure effect factor’ is derived to quantify the performance of OP2 with respect to the structure of the input matrix.
Brain Computer Interfaces: The Future of Communication Between the Brain and the External World

This paper provides a comprehensive review of the current state of research on Brain Computer Interfaces (BCIs) and their potential applications. The objective of this study was to gather information from various sources, including journal articles, conference papers, and books, to analyze the advancements and limitations of BCIs. A systematic literature review was conducted using databases such as PubMed, IEEE Xplore, and Google Scholar, with specific keywords related to BCIs and their applications. The selected studies were thoroughly analyzed to identify common themes, methodologies, and key findings. The main contributions of this review include an overview of different types of BCIs, their applications in fields such as medicine, entertainment, and education, and the challenges and limitations faced by BCI technology. The findings highlight the potential of BCIs in restoring motor function, improving the quality of life for individuals with various conditions, and enhancing human-technology interaction. Furthermore, the review identifies future research directions, including signal processing improvements, exploring hybrid and multimodal approaches, conducting long-term real-world studies, addressing ethical considerations, and prioritizing user-centred design. This comprehensive analysis of existing literature provides valuable insights for researchers and practitioners in the field of BCIs and sets the stage for future advancements in this rapidly evolving domain.
Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model

We examine the conditions under which each individual series that is generated by a vector autoregressive model can be represented as an autoregressive model that is augmented with the lags of few linear combinations of all the variables in the system. We call this modelling Multivariate Index-Augmented Autoregression (MIAAR). We show that the parameters of the MIAAR can be estimated by a switching algorithm that increases the Gaussian likelihood at each iteration. Since maximum likelihood estimation may perform poorly when the number of parameters gets larger, we propose a regularized version of our algorithm to handle a medium-large number of time series. We illustrate the usefulness of the MIAAR modelling both by empirical applications and simulations.
PolSAR image classification with Binary Partition Tree

The objective of this project is to study new techniques in the use of Binary Partition Tree to improve the classification of objects in PolSAR images. Different techniques will be developed and the results will be compared with the state of the art. Finally, some conclusions are presented and some possible lines of future work are discussed.
Optical refrigeration inches toward liquid-nitrogen temperatures

Superconductivity, longand mid-wave IR detectors, and ultrastable laser cavities that operate in the 77–150K temperature range can all benefit from vibration-free cooling.1 Currently, such low temperatures can only be achieved using cryogenic gases or liquids, solid cryogens, or mechanical refrigerators. Unfortunately, these coolers require regular attention, introduce vibrational noise, and are subject to mechanical wear over time. Many space-based applications (particularly ultra-stable laser cavities) cannot tolerate these drawbacks. All-solid-state cryocoolers are therefore desirable because of their inherent vibration-free operation and potentially long lifetime. Optical refrigeration (i.e., anti-Stokes fluorescence cooling) is the only solid-state cooling technology capable of reaching cryogenic temperatures. Anti-Stokes cooling—in which a doped crystal is excited by a laser with a wavelength that is longer than the average wavelength of the resulting fluorescence, thus leading to cooling of the crystal—was first suggested by Peter Pringsheim almost 90 years ago.2 It was not actually observed, however, until years after the invention of lasers and the availability of high-purity host materials. The first demonstration of optical refrigeration, reported in 1995, used a fluorozirconate glass doped with ytterbium (Yb). The resulting material is known as Yb3C: ZBLANP.3 Cooling occurs when low-entropy laser light (tuned to a slightly lower energy than the mean fluorescence of a material) is absorbed, thus giving rise to efficient fluorescence generation and escape. On average, each pump photon removes vibrational energy (i.e., phonons) from the cooling sample after being absorbed and re-emitted. Figure 1. Schematic of our astigmatic Herriott cell. The geometry of the cell enables laser light (red) to be trapped inside of the crystal, ensuring more than 95% absorption. R1x;y D 50cm, R2x D 1, and R2y D 50cm, where R1 and R2 are the radii of curvature of the spherical and cylindrical mirrors, respectively. x;y : Launching angle. W : Crystal length, width, and height (Wx D Wy D W ).
Zonally-magnified oblique multislice (ZOOM) EPI.

Introduction Echo-Planar Imaging (EPI) is an ultrafast technique which has the drawback of sensitivity to off-resonance effects caused by the presence of local gradients. This gives rise to geometric distortions and signal drop-outs in the EP image. The magnitude of these distortions depends on the gradient performance, field of view (FOV), and resolution of the image and as such provides an additional constraint to the resolution obtainable. Snap-shot EPI is the technique of choice for functional and diffusion studies; multi-shot navigated EPI has been used succesfully in Diffusion Tensor Imaging (l), but some images have to be discarded, and the distortions can still constrain image resolution. If only a small section of a larger sample is required then zonally-magnified EPI can reduce gradient requirements by only sampling a limited FOV. Resolution is enhanced but the shorter echo-train length gives less distortion than the equivalent full FOV acquisition. Such inner-volume excitation techniques only work in single-slice mode (Z-3), as in multislice mode successive inversions by all the orthogonal 180-degree pulses lead to a drastic signal reduction. Another approach uses 2dimensional slice selective pulses with very rectangular slice profiles to select an inner volume (4). Here we present a modified version of the zonally-magnified EPI technique which is not so dependent on the 180 pulse profile and can be used in a mutli-slice mode without loss of signal to noise.
Lesbian and bisexual women's health beliefs about performing the Papanicolaou’s test

Objective: to identify the health beliefs of lesbian and bisexual women about performing the Papanicolaou’s test. Methods: cross-sectional study developed exclusively online, with fifty-five participants. Google Forms® were used for data collection, with sociodemographic and economic questions, as well as questions related to practices, intention, and beliefs about performing the Papanicolaou’s test. Data were organized in Google Sheets® and analyzed in SPSS® software. Results: it was found that bisexual women believe more in the benefit "when I do the preventive exam, I am relieved" (p=0.047). However, they have higher scores of ashamed for doing the preventive exam (p=0.005). Significant association was identified between having done the exam and perceived benefits (p=0.040); perceived severity and education level (p=0.006); having done the exam (p=0.039); and having fixed partnership (p=0.028). Conclusion: bisexual women believe that performing the exam generates relief, but feelings of shame may hinder adherence to the exam. Lesbian and bisexual women without access to higher education, who never had the exam, and who have multiple sexual partnerships are more vulnerable to cervical cancer. Contributions to practice: reflect on this scenario so that educational strategies are effective for cervical cancer prevention.
Dimension reduction-based attributes selection in no-reference learning-based image quality algorithms

No-reference image quality metrics are of fundamental interest as they can be embedded in practical applications. The main goal of this paper is to define a new selection process of attributes in no-reference learning-based image quality algorithms. To perform this selection, attributes of seven well known no-reference image quality algorithms are analyzed and compared with respect to degradations present into the image. To assess the performance of these algorithms, the Spearman Rank Ordered Correlation Coefficient (SROCC) is computed between the predicted values and the MOS of three public databases. In addition, an hypothesis test is conducted to evaluate the statistical significance of performance of each tested algorithm.
Behavioral and Electrical Modeling of a 0.5-V Third-Order Continuous-Time Sigma-Delta Modulator with FIR DAC for Audio Applications

Most mobile and wearable devices present digital audio signal processing capabilities. Since the nature of audio signals is analog, there is a need to use analog-to-digital converters (ADCs) with high-resolution for a high signal-to-noise ratio audio acquisition. This paper presents the high-level modeling and design of a continuous-time third-order sigma-delta modulator (CT-SDM) with an FIR DAC for audio devices, using a supply voltage of 0.5 V. The design is divided in three steps and is carried out using the Delta-sigma toolbox and a discrete-time to continuous-time (DT-CT) transformation. First, the schematic implementation with verilogA models is done to estimate the first-integrator amplifier specifications for the modulator to provide 14 bits of ENOB. Following, a two-stage inverter-based amplifier is designed and used to verify the design strategy. Finally, a transistor-level implementation of OTAs and comparator is done to evaluate the CT-SDM performance. An in-depth analysis and discussion are presented to explain the achieved results with those transistor-level circuits.