Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Control Techniques for a DC-DC Boost Converter Exhibiting Non-minimum Phase Behaviour

This paper is about comparion of three well known robust control techniques used for the regulation of output voltage for a dc-dc boost converter that has a non minimum phase characteristics. These methods are a two degree of freedom internal model control(TDF-IMC), cascaded active disturbance rejection control(CADRC) and proportional integral and derivative(PID) control. These methods have quite different control structure deign, such as IMC is a model based control while ADRC does not need the exact knowledge of the model. These methods have been implemented on boost converter and based on simulation studies settling time, integral absolute error(IAE) and peak overshoot/undershoot were calculated and compared. It was found that IMC method of control proved to be better than than the other two methods. This was proved on the basis of simulation results and the comparison parameters. From this comparison, it can be concluded that IMC scheme is better suited for dc-dc boost converter.
Spatiotemporal scaling of visually evoked human neuromagnetic signals

In this work, we carry out a fractal analysis of visually evoked neuromagnetic responses of the human cerebral cortex in response to flickering light stimuli of different color combinations (red-blue, red-green, blue-green). The study uses magnetoencephalogram signals from healthy subjects recorded before and after exposure to stimuli. The modified Higuchi method is used to calculate the fractal dimension $D$. Spatiotemporal scaling is demonstrated on the basis of topographic maps and histograms. It has been shown that the inclusion of light stimuli leads to a significant reaction from almost all parts of the human cerebral cortex. The most significant change in the self-similar properties of the studied signals is observed for the frontal and occipital regions. The results obtained will be of interest to data science, neurophysiology and cognitive psychology.
Beam Coordination via Diffusion Reduced-Rank Adaptation Over Array Networks With Parametric Matrix Combination

Distributed and collaborative beamforming (DCBF) has been attracting increased interest due to its desirable scalability and enhanced robustness, whereas slow convergence would hinder practical applications of the conventional DCBF algorithms for massive arrays. In this work, we consider the distributed reduced-rank beam coordination over an array network, equipped with a collection of potentially massive arrays interconnected via a certain topology. We develop a diffusion reduced-rank beam coordination algorithm along with an inherently adaptive combination scheme based on the combination matrix for beam coordination, leveraging the joint iterative alternating optimization methodology. We propose two efficient adaptive implementations with parametric matrix combination schemes, which could further enhance the robustness of the distributed reduced-rank beam coordination algorithm against the spatial variations of the signal and noise powers. The proposed distributed reduced-rank beam coordination algorithm could not only remarkably speed the initial convergence in comparison with its full-rank counterparts under the small-sample conditions, but also achieve comparable steady-state performance with sufficiently large sample size. The tracking capability could also be enhanced with the proposed algorithm as a by-product. Illustrative simulations validate the efficiency of the proposed reduced-rank beam coordination algorithm.
Comparing Lab-based and Telephone-based Speech Recordings Towards Parkinson's Assessment: Insights from Acoustic Analysis

The use of high-quality lab-based speech recordings has led to key breakthroughs in a range of Parkinson's Disease (PD) assessment applications. We recently reported on the Parkinson's Voice Initiative (PVI) study collecting telephone-based speech recordings under non-controlled acoustic conditions towards large-scale PD assessment. In this study, we aim to compare the underlying acoustic properties of the sustained vowel /a/ recordings across two large PD datasets focusing only on US speakers to avoid any linguistic confounders. We acoustically characterized 2097 sustained vowel /a/ recordings from 1138 PD participants and compare findings against a large public high-quality speech-PD database of 5875 recordings across 16 dysphonia measures using the symmetric Kullback-Leibler divergence. We explored gender stratification and two-dimensional projections using t-distributed Stochastic Neighbor Embedding (t-SNE) to facilitate visual examination and understand database differences. We find that there are considerable differences in the distributions of the dysphonia measures both univariately and when considered in lower dimensional t-SNE projections even for the linear dysphonia measures. Collectively, these findings provide new insights into understanding the inherent challenges when aiming to generalize findings from lab-based settings to real-world practical applications towards speech- PD clinical decision support tools and may motivate the development of new speech signal processing algorithms.
Selected technical issues of deep neural networks for image classification purposes

Malignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task due to uneven datasets sizes, the huge intra-class variation with small interclass variation, and the existence of many artifacts in the image. One of the most recognized methods of melanoma diagnosis is the ABCD method. In the paper, we propose an extended version of this method and an intelligent decision support system based on neural networks that uses its results in a form of hand-crafted features. Automatic determination of the skin features used by the ABCD method is difficult due to the large diversity of images of various quality, the existence of hair, different markers and other obstacles. Therefore, it was necessary to apply advanced methods of preprocessing the images. The system is an ensemble of ten neural networks, working in parallel and one network using their results to generate a final decision. This system structure allowed us to increase the efficiency of the operation by several percentage points compared to a single neural network. The proposed system is trained on over 5000 and tested afterward on 200 skin moles. The presented system can be used as a decision support system for primary care physicians, as a system capable of self-examination of the skin with a dermatoscope and also as an important tool to improve biopsy decision making.
On operator algebras associated with monomial ideals in noncommuting variables

We study operator algebras arising from monomial ideals in the ring of polynomials in noncommuting variables, through the apparatus of subproduct systems and C*-correspondences. We provide a full comparison amongst the related operator algebras. For our analysis we isolate a partially defined dynamical system, to which we refer as the {\em quantised dynamics} of the monomial ideal. In addition we revisit several previously considered constructions. These include Matsumoto's subshift C*-algebras, as well as the tensor and the Pimsner algebras associated with dynamical systems or graphs. We sort out the various relations by giving concrete conditions and counterexamples that orientate the operator algebras of our context. It appears that the boundary C*-algebras do not arise as the quotient with the compact operators unconditionally. We establish a dichotomy to this effect by examining the resulting tensor algebras. We identify their boundary representations, we analyse their C*-envelopes, and we give criteria for hyperrigidity. Moreover we completely classify them in terms of the data provided by the monomial ideals. For tensor algebras of C*-correspondences and bounded isomorphisms this is achieved up to the level of local conjugacy (in the sense of Davidson and Roydor) for the quantised dynamics. For tensor algebras of subproduct systems and algebraic isomorphisms this is achieved up to the level of equality of monomial ideals modulo permutations of the variables. In the process we accomplish more in different directions. Most notably we show that tensor algebras form a complete invariant for isomorphic (resp. similar) subproduct systems of homogeneous ideals up to isometric (resp. bounded) isomorphisms. The results on local conjugacy are obtained via an alternative proof of the breakthrough result of Davidson and Katsoulis on piecewise conjugate systems. For our purposes we use appropriate compressions of the Fock representation. We then apply this alternative proof locally for the partially defined quantised dynamics. In this way we avoid the topological graphs machinery and pave the way for further applications. These include operator algebras of dynamical systems over commuting contractions or over row commuting contractions.
Appraising the Potential of Pre and Pilot Testing during Survey Based Research: A Case Study in Indian Banking Sector

This paper envisages the capabilities of Pre-Testing and Pilot-Testing of Questionnaires designed to conduct survey based studies. Related literature is surveyed to access the status and significance of questionnaire-testing, before carrying out regular survey based researches. It also highlights the scarcity of appropriate procedure to carry out effective testing and tends to fill this gap by performing step-wise Pre and Pilot Testing of a Questionnaire, designed to access the impact of artificial intelligence in Indian banking sector. It is a naval study skimming the vitality of both the testings, after accomplishing the proposed methodology and without ignoring the existing constraints of Indian private banking environments. Findings of this study have tremendous application in any kind of survey based study related to scientific, social and economic scenarios. From last couple of decades, Questionnaires have erupted as well structured and systematic way to gather exact voice of masses and its efficiency can be enhanced by designing and redesigning it through relevant pre and pilot testing.
An update on cooking recipe generation with Machine Learning and Natural Language Processing

Cooking is a part of most peoples’ daily routine. Yet there’s always the same question: “What are we going to cook today? ”. We will attempt to create a roundup of Machine Learning approaches whose goal is to provide an answer to that question with recipe generation by using as input just a list of preferred ingredients - or even generating them as well!
Sur les idéaux génériques de l'algèbre des fonctions sur un groupe quantique

Soit R q [G] l'algebre quantifiee des fonctions sur un groupe semi-simple G ayant W comme groupe de Weyl. On rappelle [2] que Spec R q [G] est reunion disjointe de sous-ensembles X(w): w ∈ W x W et que tout X(w) admet un unique element minimal Q w . Ici on donne une nouvelle formule pour Q w . Ceci permet une comparaison des deux descriptions de Spec R q [G] fournies dans [2] et semble etre utile dans l'etude du graphe des liaisons Γ. En particulier pour G = SL(n) on determine les mineurs quantiques appartenant a Q w . On repond ainsi a une conjecture de [1].
The Role of Animacy in Russian Morphosyntax

This dissertation investigates the effects of animacy in the syntax and morpho-syntax of language. More precisely, I examine cases in which minimal pairs, varying only in the animacy of the subject NP, show different morpho-syntactic behavior. This study suggests that a significant part of the explanation of these effects lies in the syntactic representation of argument structure—that animate and inanimate NPs, under certain conditions, receive different thematic roles, and thus occupy different structural positions at the syntactic level(s) where argument structure is represented. The findings of this project are shown to constitute a part of a broader generalization and find empirical support in a cross-linguistic perspective. One of the primary findings in this dissertation is an empirical generalization concerning animate arguments of unaccusative verbs in Russian which display a previously unnoticed type of ‘variable behavior’ for unaccusative diagnostics. I propose that animate arguments must be interpreted as Experiencers whenever possible, and can only be Themes when an Experiencer interpretation is unavailable. The thematic distinction correlates with a structural difference that explains the variable behavior under unaccusativity diagnostics. Another important contribution of this study is a uniform account of Russian and Italian data involving typical unergative predicates which are problematic for the original formulation of
A Deep Learning Approach for Tree Root Detection using GPR Spectrogram Imagery

Monitoring and conservation of natural resources have become increasingly necessary, as the impact of new diseases has created major concerns for environmentalists and communities in recent years. Within this context, tree roots are one of the plants' most important and vulnerable organs as well as one of the most challenging ones to inspect. To that effect, the non-destructive testing (NDT) methods have become one of the preferred methods of tree roots assessment and monitoring, as opposed to other conventional and destructive techniques. Within this context, applications of ground penetrating radar (GPR) have proven to be accurate and efficient for tree roots' investigation and mapping. However, a major challenge for GPR detection of tree roots is the soil inhomogeneity. This study aims to mitigate the uncertainty in root detection by proposing a deep learning method based on the analysis of GPR spectrograms (i.e., a graphic representation of a signal's frequency spectrum with respect to time). In this study, the GPR signal is first processed in both the time and frequency domains to filter the existing noise-related information and hence, to produce spectrograms. Subsequently, an image-based deep learning framework is implemented, and the effectiveness in detecting tree roots is analysed in comparison with conventional feature-based machine learning classifiers. The preliminary results of this research demonstrate the potential of the proposed approach and pave the way for the implementation of new methodologies in assessing tree root systems.
Empirical functionals and e cient smoothing parameter selection

A striking feature of curve estimation is that the smoothing parameter h 0 , which minimizes the squared error of a kernel or smoothing spline estimator, is very difficult to estimate. This is manifest both in slow rates of convergence and in high variability of standard methods such as cross-validation. We quantify this difficulty by describing nonparametric information bounds and exhibit asymptotically efficient estimators of h 0 that attain the bounds. The efficient estimators are substantially less variable than cross-validation (and other current procedures) and simulations suggest that they may offer improvements at moderate sample sizes, at least in terms of minimizing the squared error
Electrochemical Sensor Based on Mn3O4-MnCo2O4-CNT Nanocatalyst Graphene Nanoplatelets for Trace Determination of Thioridazine in Biological and Pharmaceutical Samples

In this paper, a mixed nano-catalyst of magnesium oxide (Mn<sub>3</sub>O<sub>4</sub>) and magnesium-cobalt oxide (MnCo<sub>2</sub>O<sub>4</sub>) was synthesized in the presence of carbon nanotubes (CNT) and was easily mixed with graphene nanoplatelet (GNP). The constructed nanocomposite (Mn<sub>3</sub>O<sub>4</sub>-MnCo<sub>2</sub>O<sub>4</sub>-CNT/GNP) was characterized using field emission scanning electron microscopy, transmission electron microscopy, and energy dispersive X-ray. As an application, a suspension of Mn<sub>3</sub>O<sub>4</sub>-MnCo<sub>2</sub>O<sub>4</sub>-CNT/GNP nanocomposite was prepared and drop-casted on a screen-printed electrode (SPE) for using as a portable electrochemical sensor for the determination of thioridazine (TR) with the highest catalytic effect, sensitivity, and selectivity. The differential pulse voltammetry response of the Mn<sub>3</sub>O<sub>4</sub>-MnCo<sub>2</sub>O<sub>4</sub>-CNT/GNP-modified SPE was linear with TR concentration in the range of 0.02–<inline-formula> <tex-math notation="LaTeX">$50.0~ {\mu }$ </tex-math></inline-formula>mol <inline-formula> <tex-math notation="LaTeX">$\text{L}^{- {1}}$ </tex-math></inline-formula> under optimized conditions (phosphate buffer, pH 7.0; drop-casting volume <inline-formula> <tex-math notation="LaTeX">$20~\mu \text{L}$ </tex-math></inline-formula>; nano-catalyst 200 <inline-formula> <tex-math notation="LaTeX">$\mu \text{L}$ </tex-math></inline-formula>; and accumulation time 80 s), with a detection limit of 1.5 nmol <inline-formula> <tex-math notation="LaTeX">$\text{L}^{- {1}}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$S/N = 3$ </tex-math></inline-formula>). The fabricated electrochemical sensor showed a selective amperometry response toward TR concentration among other interfering species. Relative standard deviation (RSD) at two different concentrations of TR, (10.0 and 40.0 <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>mol <inline-formula> <tex-math notation="LaTeX">$\text{L}^{- {1}}$ </tex-math></inline-formula>) was, respectively, 4.9% and 4.5%, exhibited good reproducibility. Finally, the proposed sensor was successfully applied for the determination of the TR in urine, serum, and a TR pharmaceutical formulation.
Toward Improved Data Quality in Public Health: Analysis of Anomaly Detection Tools applied to HIV/AIDS Data in Africa

The study examined the data quality efficiency of the WHO Data Quality Review (DQR) toolkit and PyCaret anomaly detection algorithms. The tools were applied to the African HIV/AIDS data (2015-2021) extracted from a public data repository (data.pepfar.gov). The research outcome suggests that unsupervised anomaly detection algorithms could complement the efficiency of the WHO DQR toolkit and improve Data Quality Assessment (DQA). In particular, the study showed that anomaly detection algorithms through python programming provide a more straightforward and more reliable process for detecting data inconsistencies, incompleteness, and timeliness appears more accurate than the WHO tool. Consequently, the study contributed to ongoing debates on improving health data quality in low-income African countries.
Design & Simulation of a High Frequency Rectifier Using Operational Amplifier

At the end of the nineteenth century and the beginning of the twentieth century, techniques of half-wave and full-wave averaging appeared. With the advent of semiconductor technology, the bridge was made of full wave rectifier diode. So that electronic circuit size became small for the waveform rectifier while improving the quality of the rectified wave. And as a result of technological development in recent decades in the field of electrical and electronic engineering and systems communications the need for a small-sized rectifier device that deals with different frequencies has emerged. In this project the high frequency rectifier was designed. This design consists of two amplifier operations with some passive components. The signal outside the design was derived, and the design was carried out by simulations using an electronic program (Multisim 13). The results obtained from simulations of this design for signals of different frequencies: 10 kHz, 100 kHz, 1 MHz, 2 MHz, 3 MHz & 5 MHz were encouraging.
Attention Layers for Temporal RNN Modeling of Speech Emotions

Recently, speech emotion recognition (SER) has become an important task in human-computer interface applications. A popular approach to achieve more effective modeling of speech emotions in recurrent neural networks is based on the use of an attention mechanism (AM). AM allows the network to search for important information in a speech sequence. Since a comparison of different types of AM computation methods has not yet been elaborated more deeply in published works, we decided to address this issue and test various attention layers to observe their effects on the accuracy of the SER system. In most cases, AM increased the recognition accuracy of the least represented class and hence, to some extent, compensated for the effect of unequal number of samples in the classes being recognized. The experimental results on the IEMOCAP benchmark dataset show that the most significant improvement was achieved by Multi-Head Attention.
Personality Traits Prediction from Text via Machine Learning

Social media platforms have been expanding their user bases. For example, LinkedIn counts 917 million monthly visitors, while Twitter has 3.62 billion monthly visitors. YouTube has 22.77 billion monthly visitors, and Instagram has 2.86 billion monthly visitors. Reports confirm data size increase of the social media networks above by 20–30% every day. With the spread of COVID-19, the same platforms have been broadly used by the worldwide collectiveness to socialize and stay amongst people. Analyzing text from Social Networking sites helps recognize individuals' personality traits automatically. A person's personality refers to their unique characteristics that shape their habits, behaviour, attitude, and cognitive tendencies. In this work, several machine learning techniques are surveyed to estimate personality traits from input text using the Myers-Briggs Type Indicator (MBTI) model. Experiments are run over a freely accessible dataset from Kaggle. In addition, techniques such as tokenization, word stemming, stop word elimination, and feature selection, utilizing TF-IDF, are used to analyze personality traits further.
Developing regional calibration coefficients for estimation of hourly global solar radiation in Ireland

ABSTRACT This study proposed regional coefficients for estimating hourly global solar radiation through the adaptation of some empirical models that relate radiation to climatological and geographical variables. A total of 10 models were adapted over 7 stations in Ireland. The performance of the models was evaluated using some selected error indicators including the global performance index which combines all other error indices. The results indicated that the sunshine based regional calibration coefficients generated through a polynomial approach was most superior over other models with the lowest RMSE (0.2–0.3 MJm−2 hr−1), MAE (0.1–0.2 MJm−2 hr−1) and Pbias (0–7.0%) and highest R2 and KGE (>0.85). The study found no local effect such as instrumental siting, observational uncertainty and climate on the variations of these coefficients. This outcome will therefore facilitate the design of various local and/or regional solar energy applications at microscale in a temperate region.
Fabrication of array of micro-holes by femtosecond laser pulses for advanced IR-sensing applications

This review discusses the discovery, the resulting large arrays with million micro-holes per array of different structure in plasmonic materials films with different thickness such as silver (Ag), copper (Cu), aluminum (AI) and alloy gold (Au/Pd) by ablation technology using femtosecond laser pulses. Based on the experimental results, the characteristics of plasmonic effects in the IR band with wavenumber 1.5-25 ${\mu} \mathbf{m}$ were verified during the extraordinary optical transmission through the array of micro-holes. Experiments have shown that a smooth transition from the common Bethe-Bouwkamp transmission to its plasmon-enhanced analogue, ending up with common geometrical (wave-guide-like) transmission, when wavenumber increases.