Miracle-dz/UniFAR-FTUs huggingface dataset:
An IoT Based Heart Healthcare Platform for the Sultanate of Oman

The evolution of the Internet of Things (IoT) has been from the convergence of different forms of digital technologies such as embedded systems, real-time analytics, wireless communication, and sensors. The rise of cardiovascular disease (CVD) among adults in Oman has become a growing concern. All IoT-driven healthcare and wellness systems facilitate a continuous form of monitoring of several chronic conditions. The use of IoT healthcare platforms has a huge positive impact in providing timely help and improvement in general well-being. An abnormal situation caused due to irregular heartbeat rate is called arrhythmia and this may become dangerous as the cardiac system is affected due to aging and other pathological and sociological factors. In order to diagnose this abnormality, electrical impulses produced by the heart are recorded by equipment called Electrocardiogram (ECG). A wearable ECG device is used to monitor the patients heartbeats through the IoT platform. The ECG signals to arrhythmia classes are classified using Convolutional Neural Networks (CNN). 1D CNN techniques is used in state-of-the-art modern research, in order to classify this signal. Tabu Search (TS) algorithm with CNN, is used in this work to classify ECG signal image. The evaluation of the technique is done based on performance evaluation matrices which can produce enhanced outcomes, when compared to the present literature.
Ground state energy for the Hartree–Fock equations with Dirichlet boundary conditions

The Hartree–Fock equations for 2 N fermions in a one‐dimensional box are solved in terms of a power series in the coupling constant for various values of N and the quantum numbers. A general formula is found for the total energy and wave function for N=1, which is accurate to sixth order. Another formula for the total energy is conjectured for any N, which is accurate to second order. Using these results, the ground state energy, energy of interaction, and boundary energy are found to second‐order accuracy for arbitrary N.
A new approach to modeling the dynamic response of Bernoulli-Euler beam under moving load

2014) Abstract. This article discusses the dynamic response of Bernoulli-Euler straight beam with angular elastic supports subjected to moving load with variable velocity. A new engineering approach for determination of the dynamic effect from the moving load on the stressed and strained state of the beam has been developed. A dynamic coefficient, a ratio of the dynamic to the static deflection of the beam, has been defined on the base of an infinite geometrical absolutely summable series. Generalization of the R. Willis’ equation has been carried out: generalized boundary conditions have been introduced; the generalized elastic curve’s equation on the base of infinite trigonometric series method has been obtained; the forces of inertia from normal and Coriolis accelerations and reduced beam mass have been taken into account. The influence of the boundary conditions and kinematic characteristics of the moving load on the dynamic coefficient has been investigated. As a result, the dynamic stressed and strained state has been obtained as a multiplication of the static one with the dynamic coefficient. The developed approach has been compared with a finite element one for a concrete engineering case and thus its authenticity has been
A Modulated Model Predictive Control for Three-Phase Three-Level T-Type Inverter

In order to control a three-phase three-level T-type inverter, a straightforward modulated model predictive control (MPC) method with a double-objective cost function is presented in this study. The modulated MPC can provide balanced dc-link capacitor voltages and three-phase sinusoidal load current under linear loads. First, 27 potential switching vectors are used to predict the future behavior of state variables. Then, using a seven-segment switching sequence similar to that used in space-vector modulation, three active voltage vectors are chosen to correspond to the lowest value of the double-objective cost function and are applied to the inverter, resulting in a fixed switching frequency operation. To assess the performance of the modulated MPC method under various load situations, simulation results are presented.
A Compact Literature Review on Stock Market Prediction

Stock market forecasting has been incredibly hard since it necessitates in-depth knowledge of news events, historical data analysis, as well as the influence of news on stock price movements. The situation is compounded much more difficult by the extreme movement of the stock price patterns. Nevertheless, there is indeed a scarcity of literature that provides a comprehensive review of a general background of stock forecasting. This report gives a complete literature review to fill up this research gap. Data preprocessing strategies, feature extraction techniques, prediction techniques, and future prospects are all covered in length in this literature review. This research is noteworthy yet unique in that it develops a structured methodology for stock price prediction and identifies the advantages and flaws of previous techniques. It covers a wide range of outstanding challenges as well as research topics that the research community may find useful.
Convex and Nonconvex Approaches for the Matrix Completion Problem

Missing data is a common occurrence in modern applications such as medical imaging and movie ratings data sets for recommendation systems. Matrix completion is a procedure for recovering the missing entries in a matrix on the basis of the known entries. It is assumed that there is a linear relationship between the rows or columns of the matrix to be recovered, i.e., a small number of rows (or columns) can be used to represent the whole matrix. The matrix completion problem cannot be solved uniquely without imposing constraints such as the low-rank assumption. There are two classes of optimization techniques for sparse matrix completion, namely convex and non-convex approaches. Under the weak assumption that the matrix entries are missing in a non-uniform and deterministic fashion, the matrix completion problem can be formulated as a non-convex bilinear optimization problem that can be solved via the alternating proximal algorithm. On the other hand, the soft-impute algorithm is a convex optimization approach that can deliver fast convergence by exploiting warm-start. In this paper, we test the (convex) soft-impute and (non-convex) alternating proximal matrix completion methods using some real data and furthermore, test [1]’s claim that non-convex approaches perform better than convex approaches. We show a comparison of the two approaches in terms of computational complexity, execution time, and prediction accuracy. In addition, we propose a systematic procedure to perform model selection in both algorithms.
Optimal Sizing and Siting of Battery Energy Storage Systems with Retired Battery

Battery energy storage system (BESS) can improve reliability with a reduced load of loss and reduce the uncertainty of photovoltaic (PV) to maintain a stable operating system in the power grid. BESS optimization refers to the sizing and siting of BESS, which is becoming more popular among consumers of cost-effectiveness, energy reduction, and demand cost. However, the use of retired batteries as BESS is a new challenge. Apart from solving the problem of battery disposal, it is also still used for providing service BESS, especially in terms of cost. Therefore, this study aims to deal with optimal sizing and siting BESS on a large power grid with retired battery or second-life battery to analyze energy shifting in reducing loss of load and PV peak shaving in the IEEE RTS-24 case study uses mixed integer linear programming (MILP) model. As a result, in the optimal BESS, retired batteries are placed on buses 6, 7, and 8, with a total capacity of 55.3 MW and 124.78 MWh. As a result, the optimal BESS can improve reliability by reducing PV curtailment costs by 66% and loss of load from cases without BESS. Moreover, BESS optimal can reduce cost BESS by 13.63% and total operation cost by 0.4% lower than case BESS without optimal.
Robust Control of Robot Manipulator with Uncertain Parameters Based on Neural Network

Aiming at the control problem of space robot with load capture, considering the influence of manipulator joint flexibility and the unknown quality of non-cooperative target, a sliding mode variable structure control method based on least square method quality identification is proposed. Firstly, a non-cooperative target quality observer is designed. Then, the dynamic model of flexible joint space robot with floating base is established by using Lagrange method and momentum moment conservation principle. Based on singular perturbation theory, the system model is approximately decomposed into fast and slow subsystems for control respectively. A hyperbolic tangent sliding mode variable structure controller is designed for the slowly varying subsystem to compensate the motion error and ensure the control accuracy; A feedback compensation control algorithm based on velocity difference is designed for the fast changing subsystem to suppress the residual vibration generated by the flexible joint and improve the robustness of the system. Finally, experiments verify the effectiveness of the method.
Microstrip Transmitarray Antenna with Three Layered 5x5 Crosslet and Square Slot Patches at 38 GHz

This research aims to design a transmitarray antenna. The three layers of Crosslet and square slot in 5×5 array is designed and tested for 38 GHz frequency. In the design, we exploit the phase characteristics of the transmission coefficient through the whole structure. A phase shift of up to 233° can be achieved by variation of the length of the Crosslet and the square slot. The antenna size of 20 mm x 20 mm was fabricated using Roger RT4003C and measured. According to the simulation results, a gain of 23.29 dBi is obtained oriented to the horizontal direction as expected from the design view of the point.
LuMee: A Centralized Smart Protector for School Children who are Using Online Education

This study was motivated by the challenges experienced by parents and guardians in ensuring the safety of children in cyberspace. In the last two or three years, online education has become very popular all over the world due to the Covid 19 pandemic. Therefore parents, guardians and teachers must ensure the safety of children in cyber space. Children are more likely to go astray and there are plenty of online programs waiting to get them on wrong track and also children who are engaging in the online education can be distracted at any moment. Therefore, parents should keep a close check on their children's online activity. Apart from that due to the unawareness of children, they tempt to share their sensitive information, chance of being a victim of phishing attacks from outsiders. These problems can be overcome through the proposed web-based system. We use feature extraction, web tracking and analysis mechanisms, image processing and name entity recognition to implement this web-based system.
Damage Assessment and Recovery in Fog-based Computing Systems

Technological improvements have been exponential in the last two decades. The advancements in networking, fog computing, and artificial intelligence have allowed us to implement the "Internet of Things" starting from small sensors connected to the Internet to enormous "smart cities". However, what is common in all of these improvements is that without data, and more precisely correct data, they would be useless. For example, machine learning training and results will be useless once a malicious or affected transaction is done because it can spread throughout the entire database. The need for data integrity made scholars research different phases of data security such as detection, prevention, and recovery. In this paper, we present an effective damage assessment and recovery algorithm in fog based systems that shortens crucial downtime caused by the recovery process after an attack.
SCL-SLAM: A Scan Context-enabled LiDAR SLAM Using Factor Graph-Based Optimization

In this paper, we present a complete LiDAR SLAM framework, SCL-SLAM, by integrating the loop closure module with the Scan Context descriptor into the tightly-coupled LiDAR-Inertial odometry FAST-LIO2. As a front-end, the direct LiDAR-Inertial odometry module efficiently and robustly produces motion estimates and undistorted scans. Toward the global localization based on 3D LiDAR scans, the lightweight Scan Context descriptor is used in the loop detection module. Additionally, the scan input is filtered through the keyframe selection module to improve the computation efficiency. As a back-end, a pose graph optimization is performed for the optimized trajectory and globally consistent map. SCL-SLAM is extensively evaluated on public datasets and a robot platform over various scales and environments. Experimental result shows that SCL-SLAM achieves higher accuracy than other state-of-art LiDAR SLAM systems and real-time performance. We also extend the proposed system to a centralized architecture SLAM framework for the robot team to use with 3D LiDAR observations.
Robust human identification using ecg: eigenpulse revisited

Biometrics, such as fingerprint, iris scan, and face recognition, offer methods for identifying individuals based on a unique physiological measurement. Recent studies indicate that a person's electrocardiogram (ECG) may also provide a unique biometric signature. Several methods for processing ECG data have appeared in the literature and most approaches rest on an initial detection and segmentation of the heartbeats. Various sources of noise, such as sensor noise, poor sensor placement, or muscle movements, can degrade the ECG signal and introduce errors into the heartbeat segmentation. This paper presents a screening technique for assessing the quality of each segmented heartbeat. Using this technique, a higher quality signal can be extracted to support the identification task. We demonstrate the benefits of this quality screening using a principal component technique known as eigenpulse. The analysis demonstrated the improvement in performance attributable to the quality screening.
Simplified Torque Modelling for Different Planer Robots Sizes

Previously, many efforts and more time were required to simulate the torque of a robot due to the complicated formulation of the torque. In this paper, simplified equations of the torque for the planar robot are derived. As well as another form of the torque formulation as a Polynomial equation is proposed. The equations of motion for a planar robot have been presented using Lagrange Equation. The proposed derivation of the torque was verified for different sizes of robots (from microscale to large-scale robots). The time simulations of angular displacement, angular velocity, angular acceleration, torque, and power are demonstrated. The robot size effect on the output torque is investigated. The results exhibit an easiness of evaluation of the torque, especially for a robot with a higher degree of freedom. Finally, it is observed the high convergence between the proposed simple formulation and the Lagrange derivation with a maximum error of 0.3 %.
Traffic Load Prediction in Markov Chains using Artificial Intelligence Techniques

The paper suggests an approach based on artificial intelligence for the evaluation and synthesis of models for predictive analysis of the complex amount of users in the queue and serviced system users in modelling Markov chain M/M/25/k in procedural selection of controllable factors. Regression modelling procedures have been carried out by Generalized Regression Neural Networks (GRNNs) and Cascaded-Forward Neural Networks (CFNNs). The results of study about the investigated neural models are presented according to an assessment of the Mean-Squared Error criterion at fixed variation ranges of the spread indicator for GRNNs and the hidden neurons in CFNNs selection procedures. The studies carried out cover the cases of specified sets of teletraffic predictors combinations applied - average request rate, service time and queue capacity. Sufficient minimization levels of MSE of the order of 10-4, 10-5 are achieved, determining the positive qualities of synthesized predictive neural models.
Predicting late delivery in Supply chain 4.0 using feature selection: a machine learning model

Late delivery is a common issue that can affect organizational performance and growth of any Supply Chain. The ability to predict delivery status would be an invaluable tool for any organization seeking to retain customers and predict their future behavior. This transition of Supply Chain performance in the context of industry 4.0 is called Supply chain 4.0. This study employed machine learning (ML) algorithms to predict whether a customer's order will be delivered late. It is presented as a comparative performance combination of Random Forest Classifier algorithms and three Feature Selection scenarios. In this experiment, the best predictors were identified using the SelectKBest and the predictive models' performance was evaluated using several metrics of Anova method's f_classif() function. The empirical results have demonstrated that feature selection has increased the model accuracy from 97.9% to 99.38%.
A comparative study of machine learning models for malware detection

Malware propagation induce a serious threat to computer security, which requires continuous detection, analysis, and up-gradation of anti-virus programs. Numerous malware detection systems have been proposed however, with advanced malware’s capability of escaping from these detection techniques while remaining undetected leaves a challenging task. Machine learning (ML) methods have been commonly exploited for malware detection and analysis. In this study performance evaluation of two broad categories of ML models; tree-based models and baseline ML models is carried out. Tree-based models include AdaBoost, Gradient boosting, XGBoost, and random forest (RF) whereas baseline classifiers comprise naïve Bayes (NB), support vector classifier (SVC), k-nearest neighbors (k-NN), and decision tree (DT). The performance of these algorithms is evaluated based on performance measures such as accuracy, precision, recall, and F-score. Furthermore, a comparison of results indicates that the accuracy of RF is better among all classifiers of two categories with an accuracy of 99.96%.
Feature Point Extraction Based on CenterNet and Its Application in Ultrasonic Automatic Diagnosis

Aiming at the problem that the neck features are not significant and it is difficult to obtain the feature points in automatic ultrasound diagnosis of human neck, a feature point extraction method based on CenterNet is studied in this paper. First of all, we introduce DeepLabV3+ network for semantic segmentation of human neck image. Then, the CenterNet network with ResNet50 as the backbone network is used to extract the key points of human neck. The position of each feature point is effectively predicted through the heatmaps, and the heatmaps Loss and feature point offset Loss are calculated based on Focal Loss and L1 Loss respectively. Finally, the proposed method is applied to the automatic ultrasonic diagnosis system of human neck. The test results show that the proposed method can effectively extract the characteristic points of human neck, with good accuracy and generalization, and can meet the requirements of automatic ultrasonic diagnosis in different populations and different environments.
Comparison of Transformer Models for Information Extraction from Court Room Records in Pakistan

The legal domain has many opportunities when it comes to improvement and innovation through computational advancements. In Pakistan, as the number of reported judgments continues to grow at a rapid rate, it has become essential to process this massive chunk of data to better meet the requirements of the respective stakeholders. However, extracting the required information from this unstructured legal text is challenging. In this paper, we have compared different variations of BERT to see which would be more suited for a machine learning system that can automatically extract information from these publicly available judgments of the Supreme Court of Pakistan. A labelled dataset comprising of thirteen entities has been created using the publicly available legal judgments from the Supreme Court. Different pre-trained BERT models, namely BERTBASE-uncased, BERTBASE-cased and LegalBERT, are then further trained and fine-tuned on the created dataset for Named Entity with F1 scores of 92.47%, 94.72% and 92.5 % respectively. The BERT models have been found to improve the F1 scores of previous studies on a dataset available from Lahore High Court, having smaller number of labels, with the F1 scores of 82.3%, 93.21% and 85.06%, respectively.
Aircraft Maneuver Intelligent Recognition: An End-to-end Regularized Autoencoder and SVM Approach

Aircraft maneuver recognition is a key issue in unmanned aerial vehicle (UAV) intelligent air combat. Aiming at inefficiency of high-dimensional time-series maneuver data analysis and low recognition accuracy of traditional methods, an end-to-end regularized autoencoder-support vector machine (RAE-SVM) method is proposed. This method combines powerful feature extraction capability of unsupervised learning based autoencoder with superior classification performance of supervised learning based SVM. According to the change rule of maneuver data and prior expert knowledge, the maneuver recognition dataset based on time period feature data is constructed. The generalization performance of RAE network and the accuracy of the model are improved by introducing regularization. The aircraft maneuver recognition model based on RAE-SVM is constructed and verified by the maneuver recognition dataset. The simulation results show that the accuracy of model recognition is as high as 92.75%. The trained model only takes 2 milliseconds to recognize a set of maneuver data and meets the near real-time requirements. Therefore, the proposed approach in this work can quickly and accurately recognize aircraft maneuver without relying on expert experience, which has certain practical value.
A Compact Highly Isolated MIMO Antenna Design for WiFi-6E & 5G Applications

A 12-ports multiple-input multiple-output (MIMO) antenna design is proposed. The design is self-isolated and integrates three frequency bands namely sub-6 GHz, WiFi-6E and millimeter-wave band. The proposed design consists of six dual-band monopole slot antennas and six dual function tapered slot antennas. The monopole slot antenna works in 3.422-3.675 GHz (5G) band and 6.25-7.15 GHz (WiFi-6E) band, while the tapered slot antenna covers the 23-30 GHz band. The tapered slot works as a decoupling structure at lower frequencies as well as a slot antenna at 24GHz and 28 GHz. The design structure has an overall volume of 103.5mm×103.5mm×0.51mm. The results show satisfactory MIMO performance. The maximum value of envelop correlation coefficient is 0.124 and the minimum isolation is more than -20 dB. The realized gain is 3 dBi, 5.7 dBi, 9 dBi, and 10.3 dBi at 3.5 GHz, 6.6 GHz, 24 GHz, and 28 GHz respectively. The proposed structure is planar, simple, and compact, thus suitable for wireless devices such as future access points.