Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting Underground Water Levels: LSTM Based Model Outperforms GRU and Decision Tree Based Models

Underground water is too important in human life from various perspectives, but water depletion is a significant problem worldwide. The underground water level is decreasing daily and often increasing in some places. In order to save water, it is essential to observe underground water levels, so we proposed a methodology based on machine learning and deep learning to forecast underground water levels. This study used machine learning algorithms, including Random Forest and XGBoost Regressor, and deep learning algorithms like LSTM and GRU. We also looked into the effects of such algorithms on time series data. In terms of Mean absolute error (MAE) and Root Mean Squared Error (RMSE) score, the LSTM-based model out-performed the GRU-based model, Random Forest Regressor, and XGBoost Regressor. In our study, deep learning algorithm-based models outperformed traditional machine learning algorithms on time series data of water level. Specifically, LSTM outperformed other models in estimating groundwater levels.
The P-Band Space Exploration Synthetic Aperture Radar (SESAR)

The Space Exploration Synthetic Aperture Radar (SESAR) is a P-band (435 MHz) radar instrument for planetary applications capable of measuring the surface and subsurface of planetary bodies at full polarimetry and at meter-scale resolution. These measurements can reveal important information about the surface evolution and geologic history of the planetary bodies, and help identify buried resources for future explorers. SESAR's architecture is based on a low power, lightweight, beamforming design, specifically developed to meet stringent requirements of planetary instruments. The SESAR developmental effort, carried out at NASA/GSFC, designed and built a prototype SAR instrument, and environmentally tested it, with the goal of maturing the SESAR technology readiness level for upcoming planetary mission opportunities.
Formation-Containment Tracking for Heterogeneous Linear Multiagent Systems Under Unbounded Distributed Transmission Delays

This article considers the formation-containment tracking problem of heterogeneous linear multiagent systems with unbounded distributed transmission delays. The multiagent system under consideration consists of a reference leader with a desired trajectory, a group of formation-leaders with a prescribed formation, and a group of followers. Novel distributed control laws are proposed for the formation-leaders and followers, respectively. It is shown that under the proposed control laws, the outputs of the formation-leaders not only achieve a prescribed time-varying formation but also track the desired trajectory generated by the reference leader, while the output of each follower converges to the convex hull spanned by the outputs of those formation-leaders. It should be pointed out that many existing results on consensus tracking, formation tracking, and containment control of linear multiagent systems with constant or bounded distributed transmission delays are included as special cases of our results. Finally, a numerical example is given to illustrate the effectiveness of the proposed controllers.
ON THE CHARACTERISTIC FUNCTIONS OF THE F AND t DISTRIBUTIONS

SUMMARY. The characteristic function mf ? exhibits recurrence properties which enable us to obtain it in series form by making use of contour integration. For odd m and even n the series is infinite; otherwise, they are finite. The characteristic function of the tn distribution is ob tained analogously. 1. Recurrence relations If X possesses the J^m> n distribution (m, n are positive integers) (Fraser, 1958, pp. 202 203), then the characteristic function fm, n(? ) of F = ? X is (/? (Jm, fw))""1 0(m9 n; t)9 where n
Digital Twins in Industry 5.0: Challenges in Modeling and Communication

Digital Twins help to optimize the overall engineering process – from requirement setting through design and operations to maintenance and evolution. In order to handle the complexity they may bring throughout the lifecycle, it is worth modeling the behavior and interactions of physical systems. The same applies to digital twins, although modeling their interaction could be more challenging than handling the interaction of merely physical systems. Furthermore, the newly emerging industrial requirements on interoperability, reconfigurability, man-machine collaboration, and self-optimizing production necessitate the application of new networking paradigms. This paper provides an overview of the expectations and feasibility of modeling, communication, and networking challenges of digital twins for the upcoming revolution of Industry 5.0.
Detection of botnet in Machine Learning

There are various options available to observe a botnet. In addition, this document provides an exhaustive overview of current strategies. The presence of vast amount of data, dwelling of botnet using Artificial intelligence calculation’s is in huge pattern. The research paper, we used AI to make classifiers through particular system stream dataset. Starting there, the pre-arranged distributions were associated on the accumulated data to compute the results. Assessment of framework stream data is utilized as a system for recognizable proof which depend upon the bundle contented subsequently providing opposition attackers used modern types of encryption and indeterminate quality to protect their bots. The results clearly show that an ordered procedure is in place to separate normal and bot traffic with very high accuracy, including a basic false positive rate. In addition, essentially every sort of botnet as it could be recognized using the normal model.
Simulated Density Currents in Idealized Stratified Environments

Abstract The effects of three distinct stratifications on density current dynamics are investigated using a nonhydrostatic numerical model: (i) a stably stratified layer underneath a deep neutrally stratified flow, representing a nocturnal boundary layer over land; (ii) a neutrally stratified layer underlying a deep stably stratified flow, representing a daytime boundary layer; and (iii) a continuously stratified atmosphere. In the first case, a weak or intermediate stratification decreases the height of density currents and increases the propagation speed. The same result holds in strongly stratified situations as long as the generated disturbances in the neighborhood of the head do not propagate away. Classical density currents occur in weak stratification, multiheaded density currents in intermediate stratification, and multiheaded density currents with solitary wave–like or borelike disturbances propagating ahead of the current in strong stratification. In the second case, the upper-layer stratificati.
Interaction-awareness based Intention Inference of Lag Vehicle in Lane Changing Decision-Making Process for Autonomous Driving

Cooperative driving behavior during lane-changing decision-making processes is expected to improve the safety of autonomous vehicles. However, since the intention of the lag vehicle driver is changeable and cannot be directly observed, the challenge of achieving cooperative driving behavior still remains. In this paper, an intention inference method that combines interaction-awareness information is proposed to infer the uncertain intention of the lag vehicle driver from multiple time-series driving data. The method is based on the Hidden Markov Model with the Gaussian Mixture Model (GMM-HMM) structure, which could enhance the inference performance by catching the feature that human drivers’ intentions cannot change instantaneously. Furthermore, variables are selected to train the proposed model based on the decision-making mechanism of both drivers with the interaction during lane-changing processes. High dimensional training data is alleviated by using the virtual collision point which could convert numerous training variables into an associative variable. Experimental results demonstrate that the proposed method can improve the inference performance and the collision avoidance performance in lane-change scenes compared with previous methods.
Stereo Visual-Inertial Odometry With Online Initialization and Extrinsic Self-Calibration

Reliable extrinsic calibration is critical to fuse camera and inertial measurement unit (IMU), which are usually used in stereo visual-inertial odometry (VIO), however, it is difficult to obtain extrinsic parameters in practice. This article proposes a stereo VIO with the capability of calibrating the unknown extrinsic parameters online. The initial values of IMU-camera and camera–camera transformations are estimated during the initial process, which fully leverages commonly observed features between stereo cameras. The pose and velocity are estimated and the extrinsic parameters are jointly refined. In addition, the accelerometer and gyroscope biases, and gravity direction are taken into account. The proposed VIO scheme has been demonstrated to be effective without prior knowledge of extrinsic parameters.
Learning Pre-Grasp Manipulation of Multiple Flat Target Objects in Clutter

Limited to the width of the robot's gripper, it is difficult to vertically capture flat objects such as books and plates, especially the flat target(s) may be one or more in clutter, and the task will be more challenging. The pre-grasp manipulation can make the object rearranged and move to the edge of table to realize it graspable. In this paper, the task is transformed into Parameterized Action Markov Decision Process, and the method is proposed to solve this problem based on deep reinforcement learning. In order to improve the data utilization, the weight sharing policy network is used to predict the sliding primitive parameters of each object, and then system selects the optimal execution object from all objects by the Q network. Considering the noise and complexity of the original image, we adopts the mask images of objects and the desktop as a kind of state of policy. Meanwhile, for enhancing action efficiency in the presence of multiple flat targets, an extra reward mechanism is added. In the simulation, our method realizes the pre-grasp manipulation with higher task success rate and fewer action steps.
Improper Gaussian Signaling for Two-User MISO-NOMA Systems Considering Hardware Impairments and Imperfect SIC

In this paper, we study the advantages of improper Gaussian signaling (IGS) with the existence of hardware impairments (HWI) and imperfect successive interference cancellation (SIC) in the downlink two-user power-domain non-orthogonal multiple access (PD-NOMA) systems. We first provide the input-output relationships for the MIMO system with HWIs and then derive the rate expressions for each user. The improper signals are assumed to be generated by widely linear precoding (WLP) and transmitted under the PD-NOMA scheme in the context of imperfect SIC. To exhibit the supremacy of IGS, we formulate two kinds of optimization problems concerned with the max-min rate and maximizing achievable sum rate (ASR) between users under the quality of service (QoS) and the total power constraints (TPC). To deal with the non-convex log-determinant objective functions, we adopt successive concave quadratic programming (SCQP) to obtain suboptimal results by iterations. Simulation results demonstrate that IGS cases outperform ZF precoders and conventional proper Gaussian signaling (PGS) counterparts in both max-min rate and ASR, particularly in high I/Q imbalance (IQI) and less efficient SIC scenarios. Besides, the difference in the performance degradation between imperfect SIC and HWIs is compared. Finally, we briefly classify the conditions where IGS could be preferable for signal transmission.
2-DOF Decoupled Discrete Current Control for AC Drives at Low Sampling-to-Fundamental Frequency Ratios

In high-performance drive systems, wide bandwidth and reference tracking accuracy of current control loop are fundamental requirements. The conventional PI controller provides robustness against the machine parameter mismatching and zero steady-state error, but its dynamic performance degrades at high speed due to the bandwidth limitation. In this article, a new control structure of discrete PI controller with a deadbeat response is proposed, which combines the advantage of conventional PI controller with deadbeat characteristics. The proposed controller shows a decoupled tracking performance of up to 15% of the switching frequency while also providing extra control freedom of the disturbance rejection, which effectively improves the system stability. Experiments show a reduction of oscillation by 30% compared to the conventional PI and the validity and applicability of the proposed control method for high-speed applications with low sampling-to-fundamental (S2F) frequency ratios.
FastGR: Global Routing on CPU–GPU With Heterogeneous Task Graph Scheduler

Running time is a key metric across the standard physical design flow stages. However, with the rapid growth in design sizes, routing runtime has become the runtime bottleneck in the physical design flow. As a result, speeding routing becomes a critical and pressing task for IC design automation. Aside from the running time, we need to evaluate the quality of the global routing solution since a poor global routing engine degrades the solution performance after the entire routing stage. This work takes both of them into consideration. We propose a global routing framework with GPU-accelerated routing algorithms and a heterogeneous task graph scheduler, called FastGR, to accelerate the procedure of the modern global router and improve its effectiveness. Its runtime-oriented version <inline-formula> <tex-math notation="LaTeX">$\text {FastGR}^{\text {L}}$ </tex-math></inline-formula> achieves <inline-formula> <tex-math notation="LaTeX">$2.489\times $ </tex-math></inline-formula> speedup compared with the state-of-the-art global router. Furthermore, the GPU-accelerated L-shape pattern routing algorithm used in <inline-formula> <tex-math notation="LaTeX">$\text {FastGR}^{\text {L}}$ </tex-math></inline-formula> can contribute to <inline-formula> <tex-math notation="LaTeX">$9.324\times $ </tex-math></inline-formula> speedup over the sequential algorithm on CPU. Its quality-oriented version <inline-formula> <tex-math notation="LaTeX">$\text {FastGR}^{\text {H}}$ </tex-math></inline-formula> offers a 27.855% improvement of the number of shorts over the runtime-oriented version and still gets <inline-formula> <tex-math notation="LaTeX">$1.970\times $ </tex-math></inline-formula> faster than the most advanced global router.
Development of peak detection algorithm using window variance for physiological variability monitoring

Physiological variability has gained importance in the assessment of autonomic function as well as monitoring of severity and prognosis of various diseases in last 5 decades. Electrocardigram or peripheral arterial pulse is generally used for deriving variability spectrum. Presently manual processing and analysis of these signals are done in the absence of a rugged peak detection method yielding no false positive with few false negatives. Continuous patient monitoring demands automatic peak detection without any manual intervention. False positive peak detection can result in erroneous variability spectrum, though false negatives can be interpolated within limits (1-5%). With above in view, an algorithm named window variance is proposed which uses a moving window of the input signal (0.3 seconds) with a shift of 0.025 seconds to locate peaks and use variance to eliminate false positives. This algorithm has been tested in 5 minutes of continuous data (with and without superimposed random noise) from 5 volunteers, comprising 1907 true peaks yielding no false positives and very few false negatives (0.1573%).
Medical Imaging System Design using Microwave Antennas and Portable Platform

When discussing medical imaging systems, microwave imaging is considered one of the emerging techniques that provide fast, safe, cost effective and noninvasive medical imaging modality compared to other conventional imaging techniques such as x-ray, magnetic resonance imaging (MRI), and computed tomography imaging (CT). The imaging in microwave imaging system can be performed by mapping the interactions of microwave photons with the medium including signal reflection, attenuation and propagation. These factors are dependent on the water or blood content of the tissue. The maps of electromagnetic properties of the tissue generated with microwave imaging is used to generate medical images for different clinical applications such as leukemia, bone degradation, tumor and traumatic brain injury (TBI) detection. This paper proposes a medical imaging system that is able to produce three-dimensional images in a much safer and cheaper way compared to conventional imaging methods. The medical imaging system proposed is a work in progress and this paper is intended to outline the design and show the preliminary medical images obtained.
Fetal Facial Expression Recognition System by Lip Distance Method

The Fetal facial expression is a continuous process related to brain development. 4D ultrasonography scan of fetal showed that expressions are developed at 24 weeks and become complex such as “pain” expressions. Fetal landmarks are not available, so we have to create the landmarks as per requirement because fatal images are not so accurate in detecting face regions like standard face images. Using matlab we create the landmark points of all images available with us. And then it is possible to compare the fetal images with landmarks. In the proposed system, the lip portion is extracted from the foetal face image, and recognition of the unborn baby’s facial expressions is done by designing and implementing a lip distance measurement algorithm for expression classification. This method improves accuracy by up to 20% compared to when the total face is considered for recognition. The landmark points of foetal images are calculated considering the eyes, nose, and lips corner points. A result is compared with the Harris key-point descriptor algorithm and the Gabor feature extraction method. The lip distance method for expression classification is very simple and gives very good results as compared to other methods. This system can be used to find abnormality symptoms in a baby before birth so that the doctor and parents are aware of the baby’s mental state before birth.
Trajectory Planning of a Humanoid One-legged Robot with Tendon Elastic Actuation during Squatting after Landing

In order to improve the energy storage effect of the humanoid one-legged robot during squatting after landing, this paper adopts tendon elastic actuation (TEA) and proposes a trajectory planning method using elastic elements for energy storage. The objective is to plan an appropriate joint trajectory and convert part of the mechanical energy into the elastic potential energy of the TEA unit, so as to reduce the negative work done by the actuator. Firstly, the TEA unit is modeled, and the relationship between the elastic potential energy change and the drive torque of the TEA unit is obtained through analysis. Secondly, a capture experiment is carried out on the landing motion of humans, and the constraint conditions of joint trajectory are obtained after data preprocessing. While satisfying the joint trajectory constraints, the energy storage requirements are transformed into joint drive torque requirements and angular acceleration requirements for trajectory planning. Finally, the simulation experiment shows that, compared with rigid actuation (RA), the advantage of TEA is that it reduces the negative peak power and accumulated negative work of the actuator, and stores 34.7% of the energy into the elastic element of the system during squatting after landing. The method adopted as well as the results obtained can provide significant assistance for the application of TEA.
The Secret Sauce of Student Success: Cracking the Code by Navigating the Path to Personalized Learning with Educational Data Mining

The growing need for tailored learning experiences in post-secondary education has resulted in the adoption of educational data mining (EDM) methodologies to derive significant insights from educational data. The existing scholarly literatures suggest the utilisation of adaptive learning algorithms that integrate various data sources, such as student demographic information, academic performance, and physiological data, to offer individualised learning experiences for students. The algorithms have the capability to modulate the tempo of educational content in response to the cognitive burden experienced by students, which is gauged by their brainwave activity. This study explores the application of predictive models, such as classification, regression, and time-series analysis, in detecting patterns and trends in past data for the purpose of forecasting students’ forthcoming academic achievements. Predictive models have the potential to assist educators in making well-informed decisions aimed at enhancing course outcomes. This research introduces an approach to course improvement analytics that utilises diverse data sources, including student academic records, demographic data, and external platforms such as social media and online forums, to optimise educational results. Through the examination of this data, academic professionals can acquire valuable knowledge regarding student involvement, achievement, and conduct. The present study establishes that the utilisation of course improvement analytics yields valuable information regarding student engagement and behaviour, thereby enabling educators to make informed decisions aimed at enhancing students’ learning outcomes.
Stock Price Prediction Using Modified Bidirectional Long Short-Term Memory and Deep Learning Models: A Case Study of Bhutan Tourism Corporation Limited Stock Data

Stock price prediction has attracted a huge attention for its potential in growing of both the company and shareholders. Many different researches have done with various methods and approaches in the recent years. In this research, stock data of Bhutan Tourism Corporation Limited (BTCL) is predicted using four different and strong models; Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional LSTM (ConvLSTM), and Gated Recurrent Unit (GRU). The four models are compared based on popular performance evaluation metrics; R-squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). After the simulations of results and the model's comparison, Bi-LSTM is seen to be outperforming other models with R2 value of 95.93%. Bi-LSTM being the best model in this paper, it is tuned with three different optimizers; adam, Adamax, and Nadam and three different activations; elu, relu, and selu. Bi-LSTM with relu activation and Nadam optimizer gives slight improvement after the tuning with R2 of 95.63% and therefore chosen as the best model of the paper.
On a new fractional integral operator convoluted with a fractional k-symbol Raina function: stability

The fractional k-symbol Raina function (k-FRF) is convoluted with a class of analytic functions in a complex domain to build a new fractional integral operator (FIO) in this attempt. We research the polynomials produced by the suggested FIO in terms of their geometry and stability. On the basis of the Hadamard product’s characteristics, sufficient criteria are shown (convolution product).