Miracle-dz/UniFAR-FTUs huggingface dataset:
Age Classification Based on Voice Using Mel-Spectrogram and MFCC

Analyzing speech signals to learn the age behind the voice can be valuable for different purposes including voice authentication or advertisements formulated on age groups. Applying diverse machine learning algorithms to reveal the main features behind age can be challenging since it requires a deep understanding of voice aging; thus, to solve this problem, we developed two methods that focus on classifying a person’s age into a group (teens, twenties, thirties, forties, fifties, and sixties) based on his/her voice. The first method was using the Mel-Spectrograms images and feeding them into a Convolutional Neural Network (CNN) model as an image classification task. The second method was extracting important acoustic features including Mel-Frequency-Cepstral-Coefficients (MFCCs), and other features like, Spectral Contrast, Spectral Roll-Off, and Spectral Bandwidth. Then, classifying those extracted features using different machine learning algorithms namely K-Nearest Neighbors (KNN) and Label Propagation (LP). They achieved an accuracy of 95%. Finally, a combination of the two methods was implemented to enhance the model’s robustness. We were able to attain an overall accuracy of 97% which is the highest in the literature for the age classification task on the Mozilla Common Voice dataset.
Independence numbers and chromatic numbers of the random subgraphs of some distance graphs

This work is concerned with the Nelson-Hadwiger classical problem of finding the chromatic numbers of distance graphs in . Most consideration is given to the class of graphs defined as follows: where is the Euclidean inner product. In particular, the chromatic number of was recently found by Balogh, Kostochka and Raigorodskii. The objects of the study are the random subgraphs whose edges are independently taken from the set , each with probability . The independence number and the chromatic number of such graphs are estimated both from below and from above. In the case when is a prime power and , the order of growth of and is established. Bibliography: 51 titles.
Hummingbird-bat hybrid wing by 3-D printing*

Hovering hummingbirds have inspired small flapping-wing aerial robots. Natural flyers, including hummingbirds and bats, undergo torsional wing deformation during flapping flight owing to complex wing structure, while previous artificial wings were relatively simple and difficult to design the torsional flexibility. In this paper, we proposed a hummingbird-bat hybrid (HBH) wing in which torsional flexibility was implemented by an available fabrication technology. The HBH wing had a torsional arm at the leading edge inspired by a torsional wrist of a hummingbird. A bat-like stretchable wing membrane was also employed not to constrain the wing torsion. The membrane was supported by wing shafts of which bending stiffness was designed based on that of the feather shaft of a hummingbird. The three-dimensional (3-D) shape of the torsional arm and wing shafts was created by 3-D printing. The effect of the torsional arm and stretchable membrane on lift generation and deformation was evaluated using an electric flapping mechanism. It was confirmed that the torsional arm actually enhanced the passive wing torsion. The stretchable wing membrane further promoted the torsion effect of the torsional arm. Consequently, the HBH wing did not increase lift, but efficacy, defined as lift per input power, was greatly improved by 14% at most compared with the wing without a torsional arm.
In-vivo proximal monitoring system for plant water stress and biological activity based on stem electrical impedance

Population growth and global warming are the main threats to food production. Food security, producing enough food for the entire population, is becoming harder, and new strategies must be applied. Smart agriculture tackles this problem by integrating field sensors and data with the farmers’ knowledge to increase crop yield and reduce resource waste.This paper proposes a system to monitor the plant water stress status. This system monitors the plant directly and does not rely on environmental sensors. Acquired data are sent to a remote server thanks to LoRa communication. The designed system is low-power and relies on a single battery with more than five years of expected lifetime. The system monitors the trunk electrical impedance of plants thanks to a relaxation oscillator with a portion of the trunk in the feedback loop. This way, changes in the impedance are reflected in changes in the oscillator frequency.Two systems were installed directly in the fields and connected to apple trees. Statistical analyses were performed on the acquired data. The correlation between the trunk frequency values and the soil water potential is above 75% for both plants.The proposed system is low-power and low-cost and could be directly adopted in the fields. It can detect the water status of plants directly, avoiding environmental sensors.
Automated Action Evaluation for Robotic Imitation Learning via Siamese Neural Networks

Despite recent advances in video-guided robotic imitation learning, many methods still rely on human experts to provide sparse rewards that indicate whether robots have successfully completed tasks. The challenge of enabling robots to autonomously evaluate whether their actions can complete complex, multi-stage tasks remains unresolved. In this work, we propose an efficient few-shot robotic learning algorithm that centres around learning and evaluating from a third-person perspective to address the aforementioned challenge. We develop a novel Siamese neural network-based robotic action-state evaluation system, named “Behavior-Outcome Dual Assessment” (BODA), in our robotic imitation learning system, so as to replace artificial evaluations from human experts in multi-stage imitation learning processes and to improve learning efficiency. In this way, one video demonstration of a target task is divided into several stages. For each stage, we design two Siamese neural network-based evaluation modules in BODA: One module focuses on action changes, and the other handles working environment changes. The two modules work together to provide a comprehensive assessment of the robot's completion of each stage from the view of both the action and working environment changes. Then, BODA is integrated within a model-based reinforcement learning framework to enable the completion of our imitation learning cycle. Extensive experiments demonstrate that the evaluation processes of BODA can automatically and accurately evaluate task completion status without human intervention. In contrast to conventional methods, BODA is able to keep the accumulation of errors within acceptable limits through self-assessment in stages.
Cross-Modal Monocular Localization in Prior LiDAR Maps Utilizing Semantic Consistency

Visual localization for mobile robots and intelligent vehicles in prior LiDAR maps can achieve high accuracy and low cost. However, algorithms for finding the cross-modal correspondences between images and LiDAR map points are not yet stable. In this paper, we propose a monocular visual localization system in prior LiDAR maps, which is based on the cross-modal registration to optimize the camera pose. To align the point clouds from vision and LiDAR map, a point-to-plane Iterative Closest Point algorithm utilizing semantic consistency is designed, and a decoupling optimization strategy is proposed to compute the affine transformation for the monocular scale ambiguity. Experiments on KITTI dataset show that utilizing the semantic consistency and geometric information of the map makes our system competitive with other methods. On the self-collected dataset, experiments on different light intensities demonstrate the robustness of the system in long-term localization tasks, and the ablation study demonstrates the effectiveness of the proposed algorithms.
Improved grey prediction model based on exponential grey action quantity

With the passage of time, it has become important to investigate new methods for updating data to better fit the trends of the grey prediction model. The traditional GM(1,1) usually sets the grey action quantity as a constant. Therefore, it cannot effectively fit the dynamic characteristics of the sequence, which results in the grey model having a low precision. The linear grey action quantity model cannot represent the index change law. This paper presents a grey action quantity model, the exponential optimization grey model (EOGM(1,1)), based on the exponential type of grey action quantity; it is constructed based on the exponential characteristics of the grey prediction model. The model can fully reflect the exponential characteristics of the simulation series with time. The exponential sequence has a higher fitting accuracy. The optimized result is verified using a numerical example for the fluctuating sequence and a case study for the index of the tertiary industry's GDP. The results show that the model improves the precision of the grey forecasting model and reduces the prediction error.
One-Class Kernel Spectral Regression for Outlier Detection

The paper introduces a new efficient nonlinear one-class classifier formulated as the Rayleigh quotient criterion optimisation. The method, operating in a reproducing kernel Hilbert subspace, minimises the scatter of target distribution along an optimal projection direction while at the same time keeping projections of positive observations distant from the mean of the negative class. We provide a graph embedding view of the problem which can then be solved efficiently using the spectral regression approach. In this sense, unlike previous similar methods which often require costly eigen-computations of dense matrices, the proposed approach casts the problem under consideration into a regression framework which is computationally more efficient. In particular, it is shown that the dominant complexity of the proposed method is the complexity of computing the kernel matrix. Additional appealing characteristics of the proposed one-class classifier are: 1-the ability to be trained in an incremental fashion (allowing for application in streaming data scenarios while also reducing the computational complexity in a non-streaming operation mode); 2-being unsupervised, but providing the option for refining the solution using negative training examples, when available; Last but not least, 3-the use of the kernel trick which facilitates a nonlinear mapping of the data into a high-dimensional feature space to seek better solutions.
Explorations in the Mathematics of Inviscid Incompressible Fluids

The main subject of this dissertation is smooth incompressible fluids. The emphasis is on the incompressible Euler equations in all of R or R, but many of the ideas and results can also be adapted to other hydrodynamic systems, such as the Navier-Stokes or surface quasi-geostrophic (SQG) equations. A second subject is the modeling of moving contact lines and dynamic contact angles in inviscid liquid-vapor-solid systems under surface tension. The dissertation is divided into three independent parts: First, we introduce notation and prove useful identities for studying incompressible fluids in a pointwise Lagrangian sense. The main purpose is to provide a unified treatment of results scattered across the literature. Furthermore, we prove several analogs of Constantin’s local pressure formula for other nonlocal operators, such as the Biot-Savart law and Leray projection. Also, we define and study properties of a Lagrangian locally compact Abelian group in terms of which some nonlocal formulas encountered in fluid dynamics may be interpreted as convolutions. Second, we apply the algebraic theory of scalar polynomial orthogonal invariants to the incompressible Euler equations in two and three dimensions. Using this framework, we give simplified proofs of results of Chae and Vieillefosse. We also investigate other uses of orthogonal transformations, such as diagonalizing the deformation tensor along a particle trajectory, and comment on relative advantages and disadvantages. These techniques are likely to be useful in other orthogonally invariant PDE systems as well. Third, we propose an idealized inviscid liquid-vapor-solid model for the macroscopic study of moving contact lines and dynamic contact angles. Previous work mostly addresses viscous systems and frequently ignores a singular stress present when the contact angle is not at its equilibrium value. We also examine and clarify the role that disjoining pressure plays and outline a program for further research.
EdgeSL: Edge-Computing Architecture on Smart Lighting Control With Distilled KNN for Optimum Processing Time

Our previous research applied a novel classification-integrated moving average (CIMA) method, an intelligence method that improves the performance of passive infrared (PIR) sensors in smart lighting to make control more comfortable for the user. However, intelligence, closely related to cloud deployments with large latency effects, contradicts the real-time nature demanded by smart lighting. This paper proposes edge smart lighting (EdgeSL) architecture, an edge-computing architecture for real-time improved CIMA smart lighting control. We developed novel intelligent control using permutation importance feature selection to improve the CIMA algorithm. Three model compression methods for the k-nearest neighbor (KNN) model are compared, including the novel knowledge distillation on KNN called DistilKNN. The KNN model is the basis of CIMA. The method allows the model to run in an edge-computing environment. The experiment was carried out by evaluating processing time in three different environments, namely edge, fog, and cloud architecture. Our test results show that DistilKNN has the best accuracy compared to other methods, including pruning and quantization, which is 0.93. After deploying the compressed model to the NodeMCU, edge computing has a lower average processing time than fog computing and cloud computing, namely 9.0, 60.1, and 207.6 ms, respectively. After going through the Shapiro-Wilk test, we learned that the three processing times are not normally distributed. So after testing with the Wilcoxon test, it is proved that the EdgeSL has the best performance, where the average processing time with the other architectures has significant differences.
A Survey of Sybil Attack Countermeasures in Underwater Sensor and Acoustic Networks

Underwater sensor and acoustic networks have several unique applications including water quality and ocean life monitoring, as well as ocean navigation and exploration. They also have peculiar physical layer characteristics with respect to operating frequency and attenuation which makes them different from terrestrial wireless sensor communication. Thus, coupled with their large cost of deployment and sensitivity, they are highly vulnerable to security attacks. For instance, a Sybil node could pretend to be at several other locations in the sparse network simultaneously, thereby deceiving legitimate nodes and infringing on the security of transmitted information. Over the last few years, researchers have studied means of preventing, detecting, and mitigating Sybil attacks for safe underwater communication under different assumptions and architectural setups. However, to our knowledge, these efforts have been scattered in the literature and concrete lessons have not been drawn from these efforts via a survey/review on this subject towards achieving safe underwater communication. This motivates the presentation of this paper that provides an exposition of the academic discussion on the solutions for addressing Sybil attacks in underwater wireless communication, with respect to attack prevention, detection and mitigation while identifying some of their limitations. Similarly, proposed methods and technical aspects peculiar to these works are identified, and a wide range of challenges, opportunities, and recommendations are provided.
The Arizona Radio Observatory CO Mapping Survey of Galactic Molecular Clouds. VI. The Cep OB3 Cloud (Cepheus B and C) in CO J = 2–1, 13CO J = 2–1, and CO J = 3–2

We present (1) new fully sampled maps of CO and J = 2–1 emission and CO J = 3–2 emission toward the molecular clouds Cep B and C, associated with the Cep OB3 association; (2) a map of extinction, AV, derived from IR colors of background stars; and (3) the distribution of young stellar objects (YSOs) over the same field as the molecular maps. An LTE analysis of the CO and maps yields the distribution of molecular column densities and temperatures. Substantial variations are evident across the clouds; smaller subregions show correlations between molecular properties and dust extinction, consistent with a picture of outer photodissociation regions with a layer of CO-dark molecular gas, a CO self-shielded interior, and an inner cold dense region where CO is largely depleted onto grains. Comparing the distribution of YSOs with molecular gas surface density shows a power-law relation very similar in slope to that for the giant molecular cloud associated with the H ii region Sh2-235 from a previous paper in this series that employed the same methodology. We note the presence of several compact, isolated CO emission sources in the J = 3–2 maps. The gas temperature and velocity dispersion yield a map of the sonic Mach number, which varies across the cloud but always exceeds unity, confirming the pervasiveness of supersonic turbulence over length scales ≳0.1 pc (the map resolution). We also compute a J = 2–1 CO X-factor that varies with position but is, on average, within 20% of the Galactic average derived from CO J = 1–0 observations.
Performance Analysis of Gateway-Relayed Low Earth Orbit Satellite Networks

In this work, the realistic performance analysis of low earth orbit (LEO) satellite communication network is performed in terms of coverage probability in the communication scenario which includes stochastic geometry-based modeling of LEO satellites and gateways (GWs). The positions of LEO satellites and GWs are modeled with Binomial point process and Poisson point process, respectively. The realistic coverage performance results of interference-limited LEO satellite communication systems are obtained under the interference effect. Thus, the performance analysis of LEO satellite communication network is carried out in a more realistic way. Also, the extensive simulation results are examined in terms of various system parameters including the number of satellites, the number of available channels and GW density.
Soil moisture–precipitation coupling over Central Europe: Interactions between surface anomalies at different scales and the dynamical implication

The sign of soil moisture–precipitation coupling and its scale dependence are controversial issues when examining soil–atmosphere interactions. Using the operational convection‐permitting COSMO model with 2.8 km horizontal grid spacing, the interplay of soil‐moisture heterogeneities on different scales and the role of the midtropospheric background wind are addressed by performing a series of real‐case simulations for 11 synoptically weakly and moderately forced summer precipitation events across Central Europe. The cumulative effect of soil‐moisture bias and heterogeneities on different scales is investigated, with consideration of the prevailing synoptic forcing, by explicitly perturbing soil‐moisture conditions. A soil‐moisture bias of ±25% is combined with different soil‐moisture heterogeneity length‐scales ranging from 30 to 140 km introduced by chessboard patterns.
Anti-windup compensation for a class of iterative learning control systems subject to actuator saturation

This paper proposes a dynamic anti-windup scheme for a class of iterative learning control (ILC) systems. The anti-windup compensator has the same structure as a class of compensators for 1D systems and is able to guarantee similar properties: (i) that the constrained system with anti-windup compensation is exponentially stable if a certain linear matrix inequality is satisfied; and (ii) if the trajectory to be tracked by the nominal ILC controller is consistent with the control constraints, the anti-windup compensator will ensure that the behaviour of the nominal ILC controller is eventually recovered.
AnyQ: An Evaluation Framework for Massively-Parallel Queue Algorithms

Concurrent queue algorithms have been subject to extensive research. However, the target hardware and evaluation methodology on which the published results for any two given concurrent queue algorithms are based often share only minimal overlap. A meaningful comparison is, thus, exceedingly difficult. With the continuing trend towards more and more heterogeneous systems, it is becoming more and more important to not only evaluate and compare novel and existing queue algorithms across a wider range of target architectures, but to also be able to continuously re-evaluate queue algorithms in light of novel architectures and capabilities.To address this need, we present AnyQ, an evaluation framework for concurrent queue algorithms. We design a set of programming abstractions that enable the mapping of concurrent queue algorithms and benchmarks to a wide variety of target architectures. We demonstrate the effectiveness of these abstractions by showing that a queue algorithm expressed in a portable, high-level manner can achieve performance comparable to hand-crafted implementations. We design a system for testing and benchmarking queue algorithms. Using the developed framework, we investigate concurrent queue algorithm performance across a range of both CPU as well as GPU architectures. In hopes that it may serve the community as a starting point for building a common repository of concurrent queue algorithms as well as a base for future research, all code and data is made available as open source software at https://anydsl.github. io/anyq.
CFD modelling approaches against single wind turbine wake measurements using RANS

Numerical simulations of two wind turbine generators including the exact geometry of their blades and hub are compared against a simplified actuator disk model (ADM). The wake expansion of the upstream rotor is investigated and compared with measurements. Computational Fluid Dynamics (CFD) simulations have been performed using the open-source platform OpenFOAM [1]. The multiple reference frame (MRF) approach was used to model the inner rotating reference frames in a stationary computational mesh and outer reference frame for the full wind turbine rotor simulations. The standard k — ε and k — ω turbulence closure schemes have been used to solve the steady state, three dimensional Reynolds Averaged Navier- Stokes (RANS) equations. Results of near and far wake regions are compared with wind tunnel measurements along three horizontal lines downstream. The ADM under-predicted the velocity deficit at the wake for both turbulence models. Full wind turbine rotor simulations showed good agreement against the experimental data at the near wake, amplifying the differences between the simplified models.