Miracle-dz/UniFAR-FTUs huggingface dataset:
A comprehensive survey of automatic dysarthric speech recognition

The need for automated speech recognition has expanded as a result of significant industrial expansion for a variety of automation and human-machine interface applications. The speech impairment brought on by communication disorders, neurogenic speech disorders, or psychological speech disorders limits the performance of different artificial intelligence-based systems. The dysarthric condition is a neurogenic speech disease that restricts the capacity of the human voice to articulate. This article presents a comprehensive survey of the recent advances in the automatic dysarthric speech recognition (DSR) using machine learning (ML) and deep learning (DL) paradigms. It focuses on the methodology, database, evaluation metrics, and major findings from the study of previous approaches. From the literature survey it provides the gaps between exiting work and previous work on DSR and provides the future direction for improvement of DSR. The performance of the various machine and DL schemes is evaluated for the DSR on UASpeech dataset based on accuracy, precision, recall, and F1-score. It is observed that the DL based DSR schems outperforms the ML based DSR schemes.
Natural language understanding challenges for sentiment analysis tasks and deep learning solutions

When it comes to purchasing a product or attending an event, most people want to know what others think about it first. To construct a recommendation system, a user's likeness of a product can be measured numerically, such as a five-star rating or a binary like or dislike rating. If you don't have a numerical rating system, the product review text can still be used to make recommendations. Natural language comprehension is a branch of computer science that aims to make machines capable of natural language understanding (NLU). Negative, neutral, or positive sentiment analysis (SA) or opinion mining (OM) is an algorithmic method for automatically determining the polarity of comments and reviews based on their content. Emotional intelligence relies on text categorization to work. In the age of big data, there are countless ways to use sentiment analysis, yet SA remains a challenge. As a result of its enormous importance, sentiment analysis is a hotly debated topic in the commercial world as well as academic circles. When it comes to sentiment analysis tasks and text categorization, classical machine learning and newer deep learning algorithms are at the cutting edge of current technology.
Comparison of Low-Frequency-Commutation Modulation Techniques in a Symmetrical Cascaded H-Bridge Multilevel Inverter

Among the power inverters, modular multilevel converters (MMC) have gained popularity due to their advantages over conventional two-level inverters. Although the MMC utilizes more power switches than traditional inverters, the former exhibits notable beneﬁts such as low total harmonic distortion, low switching losses with lower voltage stress, high power quality, reduced electromagnetic interface, and modularity with numerous degrees of freedom to synthesize output waveforms. Concerning the synthesized voltage quality, several low-frequency modulation techniques have been proposed to produce output voltage with low harmonic content. A particular case of study consisting of a three-phase even-level MMC inverter is taken as a benchmark to comparatively evaluate four methods for synthesizing high-quality output voltage. The employed techniques are ( i ) selective harmonic elimination (SHE), ( ii ) generalized SHE, ( iii ) THD Voltage Minimization, and ( iv ) Optimum Nearest Level Modulation, selected based on the phase and line-to-line output voltage THD. The model is formulated to compute the commutating angles to control the MMC submodules. Simulations show individual performance under the same operating conditions. A lab-scale prototype is built to corroborate the theoretical approach. The results allow the selection of the most convenient modulation technique based on the number of commutations and harmonic spectrum.
Use of very high resolution satellite imagery

Satellite imagery is available for civilian applications since the launch of the first Landsat satellite, named at first ERTS, in 1972. The main application of this type of satellites has been the classification of the imaged objects, so a good spectral, but not a high geometric resolution was required. With SPOT 1, launched in 1986, the first civilian satellite for mapping purposes was designed. In the following only the civilian systems usable for mapping purposed are taken into account. The specification "very high resolution" is not fixed and it is used with different values. I will not use it not for the spectral resolution but only for the geometric resolution, that means the ground pixel size or more exact, the distance of the centres of projected neighboured pixels, the ground sampling distance (GSD). Only the systems usable for mapping applications are respected. Because of the development also SPOT will be included even if it is belonging to the high, but not to the very high resolution systems.
Exotic searches with the ATLAS detector

The summary is given for the exotic, non-SUSY searches for new physics with the ATLAS detector at Large Hadron Collider(LHC). The results presented use data collected at center-of-mass energies of s=13TeV, for data sets corresponding to a variety of integrated luminosities. Searches using leptons, photons, missing transverse energy, and jets are performed, as well as searches requiring custom jet and track reconstruction, and searches for so-called lepton jets. No deviations from Standard Model expectations are observed, hence constraints are placed on the phase space of available theoretical models.
A Review Paper on Raspberry Pi and its Applications

: Raspberry Pi, an efficient and powerful minicomputer having the dimension approximately equal to the size of a credit/debit card. It was invented by the United Kingdom Raspberry Pi foundation with the hope of enlightening and empowering the generation of learners to be more creative and efficient. Since its launch, many open-source communities have contributed towards open-source operating systems (OS), apps and various other forms of computers which are similar to Raspberry Pi. Moreover, various embedded system scholars and researchers across the globe are constantly involved in the development of innovative projects using this module which is observed to have out-of-the-box application. Since its inception, Raspberry Pi is under constant up-gradation both in terms of both software and hardware which is thereby making it a “Full-Fledged Computer” with a possibility to compute intense task within a specific timeframe. This review paper shall lay a foundation stone to numerous open-source community and will enable the embedded students to develop projects to a whole new level. Also, we will discuss the application/projects that have built to date.
Energy Efficient Channel Coding of Physiological Signals in Wireless Body Area Networks

An energy-efficient Forward Error Correction code (FEC), which is presented in this article, is designed specifically for wireless body area network (WBAN) with a focus towards pervasive healthcare applications. WBAN consists of wireless sensor nodes attached to the human body to monitor vital signs such as body temperature, activity or heart-rate. The network adopts a master-slave architecture, where the bodyworn slave node periodically sends sensor readings to a central master node. Similar to other wireless sensor network protocols, one of the primary design goals is to achieve low power consumption. Thus, for the wireless communication between sensor nodes on the body, no FEC is normally applied and redundancy is avoided. When error occurs, in noisy wireless channels, the entire message is retransmitted. We designed a channel coding mechanism based on a Residue Number System (RNS) which can convert a binary weighted value to smaller independent residues. The residue to binary converter is the main energy consuming part of RNS. In this research, the reverse converter for a five-moduli set is presented based on the Mixed Radix Conversion method. This converter uses the formulated multiplicative inverse values in order to simplify the calculations and the required hardware for its implementation. The hardware requirements and conversion time of the proposed reverse convertor are calculated and compared with the other moduli set to illustrate its efficiency. General Terms Reliability, Channel Coding, Energy Efficiency, Body Area Networks.
Individual prediction tendencies are related but not the same across modalities

Predictive processing theories, which model the brain as a “prediction machine”, explain a wide range of cognitive functions, including learning, perception and action. Furthermore, it is increasingly accepted that aberrant prediction tendencies play a crucial role in psychiatric disorders. Given this explanatory value for clinical psychiatry, prediction tendencies are often implicitly conceptualised as individual traits or as tendencies that generalise across situations. As this has not yet explicitly been shown, in the current study, we quantify to what extent the individual tendency to anticipate sensory features of high probability generalises across modalities. Using magnetoencephalography (MEG), we recorded brain activity while participants were presented a sequence of four different (either visual or auditory) stimuli, which changed according to predefined transitional probabilities of two entropy levels: ordered vs. random. Our results show that under conditions of low entropy, stimulus features of high probability are preactivated in the visual and auditory modalities. Crucially, however, the magnitude of the tendency to predict sensory events seems to correlate only weakly between the two modalities. Furthermore, reliability statistics indicate poor internal consistency, suggesting that the measures from the different modalities are unlikely to reflect a single, common cognitive process. In sum, our findings suggest that quantification and interpretation of individual prediction tendencies cannot be generalised across modalities.
Motion Capture of Deformable Surfaces in Multi-View Studios

In this thesis we address the problem of digitizing the motion of three-dimensional shapes that move and deform in time. These shapes are observed from several points of view with cameras that record the scene's evolution as videos. Using available reconstruction methods, these videos can be converted into a sequence of three-dimensional snapshots that capture the appearance and shape of the objects in the scene. The focus of this thesis is to complement appearance and shape with information on the motion and deformation of objects. In other words, we want to measure the trajectory of every point on the observed surfaces. This is a challenging problem because the captured videos are only sequences of images, and the reconstructed shapes are built independently from each other. While the human brain excels at recreating the illusion of motion from these snapshots, using them to automatically measure motion is still largely an open problem. The majority of prior works on the subject has focused on tracking the performance of one human actor, and used the strong prior knowledge on the articulated nature of human motion to handle the ambiguity and noise inherent to visual data. In contrast, the presented developments consist of generic methods that allow to digitize scenes involving several humans and deformable objects of arbitrary nature. To perform surface tracking as generically as possible, we formulate the problem as the geometric registration of surfaces and deform a reference mesh to fit a sequence of independently reconstructed meshes. We introduce a set of algorithms and numerical tools that integrate into a pipeline whose output is an animated mesh. Our first contribution consists of a generic mesh deformation model and numerical optimization framework that divides the tracked surface into a collection of patches, organizes these patches in a deformation graph and emulates elastic behavior with respect to the reference pose. As a second contribution, we present a probabilistic formulation of deformable surface registration that embeds the inference in an Expectation-Maximization framework that explicitly accounts for the noise and in the acquisition. As a third contribution, we look at how prior knowledge can be used when tracking articulated objects, and compare different deformation model with skeletal-based tracking. The studies reported by this thesis are supported by extensive experiments on various 4D datasets. They show that in spite of weaker assumption on the nature of the tracked objects, the presented ideas allow to process complex scenes involving several arbitrary objects, while robustly handling missing data and relatively large reconstruction artifacts.
INCREASE IN LOSSES COEFFICIENT WITH AGE FOR SMALL DIAMETER PIPES. TECHNICAL NOTE

The author examines the hydraulic behavior of galvanized iron and copper pipes of ages 15 to 50 years and up to 50.8 mm (2 in.) in diameter. In galvanized iron pipes, the author discovered that the diameter is reduced and the roughness and the losses coefficient increase with age. The increase in roughness is significant and follows a parabolic law instead of the linear one cited frequently in the literature. For copper pipes, no appreciable change is evident in these variables with age. The apparition of local narrowness, or stenosis, due to age may lead to significant increases in the losses coefficient and could even close the pipe completely.
The phylogeography and post-glacial expansion of the boreal chickadee (Poecile hudsonicus)

iii ACKNOWLEDGEMENTS iv LIST OF TABLES vii LIST OF FIGURES viii LIST OF ABBREVIATIONS x CHAPTER ONE: General Introduction 1.1 The Pleistocene Epoch 1 1.1.1 The last ice age 2 1.1.2 Glacial refugia 3 1.1.3 Colonisation 5 1.2 Physical Barriers 7 1.2. 1 Mountain ranges 7 1.2. 2 Waterways 8 1.2. 3 Human-mediated barriers 9 1.3 Molecular Markers 9 1.3. 1 Mitochondrial DNA 10 1.3. 2 Microsatellites 12 1.4 Study Species 13 1.4. 1 Paridae 13 1.4. 2 The boreal chickadee 14 1.4. 3 Subspecies 16 1.5 Thesis Aims 17 1.6 Predictions 18 1.7 Thesis Organisation 19 CHAPTER TWO: The Phylogeography of the Boreal Chickadee (Poecile hudsonicus) 2.
AprilTag-Aided Pose Estimation Optimization of Landmark VSLAM

For visual Simultaneous Localization and Mapping (VSLAM), the camera positional estimation is deeply affected by environmental factors such as the surrounding feature situation and light condition. In this case, the accuracy and robustness of the positional estimation in visual odometry can be improved by the assistance of artificial landmarks. Based on this, a correction method based on AprilTag is proposed for ORB-SLAM3 to optimize the pose estimation of landmark VSLAM. Firstly, a point cloud map is built with Tag mapping embedded through the high accuracy 6 DOF positional transformation data provided by AprilTag. With this, the tracking module of ORB-SLAM3 is improved the positioning accuracy during pose estimating. In this way, the computational resources for estimating the camera pose can be reduced while guaranteeing the accuracy of pose estimation. Through subsequent validation experiments, the effectiveness of AprilTag-aided solution for ORB-SLAM3 is demonstrated for pose estimation optimization of landmark VSLAM.
Multiobjective Identification of a Feedback Synthetic Gene Circuit

Kinetic (i.e., dynamic) semimechanistic models based on the first principles are particularly important in systems and synthetic biology since they can explain and predict the functional behavior that emerges from the time-varying concentrations in cellular components. However, gene circuit models are nonlinear higher order ones and have a large number of parameters. In addition, experimental measurements are often scarce, and enough signal excitability for identification cannot always be achieved. These characteristics render the identification problem ill-posed, so most gene circuit models present incomplete parameter identifiability. Thus, parameter identification of typical biological models still appears as an open problem, where ensemble modeling approaches and multiobjective optimization arise as natural options. We address the problem of identifying the stochastic model of a closed-loop synthetic genetic circuit designed to minimize the gene expression noise. The model results from the feedback interaction between two subsystems. Besides incomplete parameter identifiability, the closed-loop dynamics cannot be directly identified due to the lack of enough input signal excitability. We apply a two-stage approach. First, the open-loop averaged time-course experimental data are used to identify a reduced-order stochastic model of the system direct chain. Then, closed-loop steady-state stochastic distributions are used to identify the remaining parameters in the feedback configuration. In both cases, multiobjective optimization is used to address the parameter identifiability, providing sets of parameters valid for different state-space regions. The methodology gives good identification results, provides clear guidelines on the effect of the parameters under different scenarios, and it is particularly useful for easily combining time-course population averaged and steady-state single-cell distribution experimental data.
Classification and Identification of Apple Leaf Diseases and Insect Pests Based on Improved ResNet-50 Model

Automaticidentification and prevention of leaf diseases and insect pests on fruit crops represent a key trend in the development of smart agriculture. In order to address the limitations of existing models with low identification rates of apple leaf diseases and insect pests, a novel identification model based on an improved ResNet-50 architecture was proposed, which incorporated the coordinate attention (CA) module and weight-adaptive multi-scale feature fusion (WAMFF) to enhance the ResNet-50’s image feature extraction capabilities. Transfer learning and online data enhancement are employed to boost the model’s generalization ability. The proposed model achieved a top-1 accuracy rate of 98.32% on the basis of AppleLeaf9 datasets, which is 4.58% higher than the value from the original model, and the improved model can effectively improve the localization of lesion features. Furthermore, compared with mainstream deep networks, such as AlexNet, VGG16, DenseNet, MNASNet, and GoogLeNet on the same dataset, the top-1 accuracy rate increased by 7.3%, 3.19%, 4.98%, 6.04% and 3.87%, respectively. The experimental results demonstrate that the improved model is effective in improving the identification accuracy of apple leaf diseases and insect pests and enhancing the model’s effective feature extraction capabilities.
$H_{\infty}$ State Estimation for State-Saturated Complex Networks with Markov-Based Nonuniform Sampling

This article investigates the $H_{\infty}$ state estimation issue for a class of complex networks subject to state saturations. A Markov chain is introduced to model the nonuniform sampling of the measurement output. With aim at estimating the network state, a series of state estimators are constructed in light of the nonuniformly sampled measurements sent by sensors. By employing the Lyapunov stability method, a sufficient condition is presented to ensure that the resulting error system is stochastically stable and the prescribed $H_{\infty}$ performance index is also satisfied. Subsequently, the gain matrices of the expected estimator are obtained by solving certain matrix inequalities. Finally, a simulation example is given to show the state estimation method's effectiveness.
Impact of Visceral Fat Area on Intraoperative Complexity and Surgical Approach Decision for Robot-Assisted Partial Nephrectomy: A Comparative Analysis with BMI

Background Optimizing surgical approaches for robot-assisted partial nephrectomy (RAPN) is vital for better patient outcomes. This retrospective study aimed to examine how visceral fat area (VFA) and body mass index (BMI) correlate with intraoperative complexities, thereby guiding the selection of surgical techniques for RAPN. Material/Methods The study analyzed the medical records of 213 Chinese patients diagnosed with a range of benign and malignant renal neoplasms and treated with RAPN in 2020. Visceral fat area was quantified using computed tomography (CT) scans taken at the umbilical level. Various perioperative indicators, such as demographic details, clinicopathological parameters, operation time, estimated blood loss (EBL), warm ischemic time (WIT), and intraoperative complications, were assessed. Results For the retroperitoneal approach, patients with either visceral obesity or general obesity had longer operation times (P<0.001 and P=0.004) and had a tendency for higher EBL (P=0.003 and P=0.001) compared to non-obese patients. In the transperitoneal approach, those with visceral obesity had significantly longer operation times (P=0.008) than their non-viscerally obese counterparts; however, general obesity showed no impact on operation time (P=0.251). Estimated blood loss was higher for patients with visceral obesity (P=0.004), but no significant difference was noted among those with general obesity (P=0.980). Conclusions VFA appears to offer predictive advantages over BMI in assessing intraoperative complexities for transperitoneal RAPN. When used in conjunction with BMI, it could serve as a valuable tool in selecting the most appropriate surgical approach for RAPN.
COVID-19 Vaccination and Serological Profile of a Brazilian University Population

Background: COVID-19 led to the suspension academic activities worldwide, affecting millions of students and staff. Methods: In this study, we evaluated the presence of IgM and IgG anti-SARS-CoV-2 antibodies in an academic population during the return to classes after a one-year suspension. The study took place over five months at a Brazilian university and included 942 participants. Results: We found that most participants had reactive IgG and non-reactive IgM. All received at least one dose, and 940 received two or more doses, of different COVID-19 vaccines. We obtained a higher average of memory antibodies (IgG) in participants who received the CoronaVac/ChAdOx1 combination. IgG was consistently distributed for each vaccine group, but individuals who completed the vaccination schedule had higher levels. There were no differences between antibodies and gender, presence of symptoms, and previous COVID-19 infection, but older participants (>53 years) and contacts of infected individuals had higher IgM levels. Conclusion: This study makes significant contributions to the assessment of antibodies in the academic environment, allowing us to infer that most participants had memory immunity and low indications of recent infection when returning to face-to-face classes, as well as demonstrating the need to monitor immunity and update vaccinations.