Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty Quantification Analysis of Wind Power: A Data-Driven Monitoring-Forecasting Framework

Advances in wind power system modeling have produced widespread socioeconomic benefits for alleviating global environmental problems. However, previous studies mainly payed attention to point forecasts of wind power system, with the absence of its uncertainty quantification analysis and outlier detection, which cannot facilitate further development in this field. In this paper, a novel monitoring-forecasting system, including the analysis module, the outlier detection module, the probabilistic forecasting module, and the evaluation module, is proposed for uncertainty modeling of wind power. In the analysis module, recurrence analysis techniques are developed, aiming at characterizing complicated patterns of wind power. Furthermore, the interval partitioning-based isolation forest algorithm, which can effectively address the effects of swamping and masking, is first developed in the outlier detection module for wind power. Superior to the traditional point forecasting method that cannot perform quantitative characterization of the intrinsic uncertainties in wind power forecasting, an advanced probabilistic forecasting method based on Gaussian process regression (GPR) with an optimal kernel function scenario, cooperating with a feature selection method, is first presented in the probabilistic forecasting module, indicating that the forecast skill of GPR is significantly enhanced. Finally, the proposed system is validated using real wind power data with high resolution from Spain in the evaluation module, solidly demonstrating its high reliability and flexibility compared to benchmarks considered in this study.
Lifetime Evaluation of Switching Devices for High-Power ELF Inverter

ELF inverter is widely used in deep ground resource detection, earthquake prediction as well as underwater communication. Because ELF inverter is of high power level and operates at extremely low-frequency range lower than 30Hz even at 0.1 Hz, it requires relatively high reliability to ensure long-term stable operation. Since the operation frequency of ELF inverter is of extremely low-frequency, the reliability evaluation of switching devices in common cases will not be applicable. This paper analyzes the instantaneous power loss of switching devices over time at different ELF operation frequency under 1kW. Based on switching devices thermal model, the variation of junction temperature over time at different ELF operation frequency is obtained and verified by simulation results. Lifetime of switching devices at different ELF operation frequency is evaluated using Coffin-Manson-Arrhenius model. Analysis and simulation results can illustrate the influence of ELF operation frequency on the thermal behavior and lifetime of switching devices providing guidance for high-power ELF inverter design and maintenance.
Chatbot - Attendance and Location Guidance system (ALGs)

Well, Timely prediction and decision making are most indispensable in this modern era of internet of things (IoT). In this paper we propose a prototype of a CHATBOT for attendance and location guidance system (ALGs) using a raspberry pi-based computer. The proposed prototype autonomously marks attendance for the registered/known students and a guidance system for unknown students/parents in a school or university. The CHATBOT includes a webcam with Haar-Cascade classifier using OpenCV machine learning cross platform library for face recognition and a Google API for speech recognition. This speech recognition API converts the voice from the microphone into written text (speech into text) and vice versa. The prototype also includes an additional switch module, preferably when the user’s microphone fails or unable to detect the user’s input voice. The switch module comes with a set of 6 push button switches with predefined questions for each switch. The prototype is tested and provides good results for all the different use cases.
Comprehensive Performance Assessment of Various NN-based Side-Slip Angle Estimators (ANN-SSE)

This paper addresses the problem of sensors lack that can directly measure the side-slip angle of a vehicle which is crucial in controlling autonomous vehicles. Therefore, estimating the side-slip angle is considered to be the most sufficient and cheapest solution. The main aim is to compare the performance of different Neural Network NN-based estimators through comprehensive performance assessment criteria. This can be done if a proper architecture is designed to allow the adaptation of the estimator to suit any tested data. Four NN approaches are investigated in this work; Feed-Forward Neural Networks (FFNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) units, and Gated Recurrent Units (GRU). The performance of the approaches is compared to measure the system accuracy represented by Root Mean Square Error and the error variance. Additionally, the computational effort of the architecture is evaluated in terms of the training time and the estimation time. Moreover, the ability of the architecture adaptation to any uncertainty in the validation data is addressed by tuning the network hyper-parameters. IPG CarMaker aided the evaluation process, by providing different roads and cars, that nearly replicates real-life conditions. The tested data show promising results in terms of accuracy, computation effort, and the robustness of the architecture. Regarding the performance assessment, it is shown that the FFNN achieves higher accuracy compared to the NN with recurrence property. Meanwhile, the GRUs surpassed the FFNN in terms of mean training time.
Decentralized Planning-Assisted Deep Reinforcement Learning for Collision and Obstacle Avoidance in UAV Networks

In this paper, we propose using a decentralized planning-assisted approach of deep reinforcement learning for collision and obstacle avoidance in UAV networks. We focus on a UAV network where there are multiple UAVs and multiple static obstacles. To avoid hitting obstacles without severely deviating from the ideal UAV trajectories, we propose merging adjacent obstacles based on convex hulls and design a novel trajectory planning algorithm. For UAVs to efficiently avoid collisions in a distributed manner, we propose using a decentralized multi-agent deep reinforcement learning approach based on policy gradients. In addition, we propose using a priority-based algorithm for avoiding collisions without reducing the speeds of UAVs too much. Simulation results show that the proposed decentralized planning-assisted deep reinforcement learning approach outperforms a number of baseline approaches in terms of the probability that all UAVs successfully reach their goals within the deadline.
Localization Estimation of Magnetic Targets Using Airborne Magnetic Anomaly Detection

The paper investigates the characterization of magnetic targets using the airborne magnetic anomaly detection. The magnetic field magnitude of a magnetic dipole is mathematically deduced and discussed. And the airborne magnetic anomaly signal model is established. The characterization of the magnetic target, i.e., the closest proximity approach, is estimated using the magnetic field magnitude by a real-time nonlinear regression algorithm. Computer simulations are performed to validate the proposed scheme. Results demonstrate that the procedure can yield a satisfactory solution with the mean localization error of 7.0% for SNR=0. The positive outcome renders this scheme an alternative potential candidate for practical MAD remote sensing applications.
A Novel Intrusion Detection System for RPL-Based Cyber–Physical Systems

The physical layer of cyber–physical systems (CPSs) is composed of resource-constrained devices connected in a wireless sensor network (WSN). Although this layer is easy to deploy, in most cases, it has many security issues. Several intrusion detection systems (IDSs) have been proposed and tested as effective and efficient solutions to detect only a few known attacks. In this article, we propose a novel, Supervised machine learning-based IDS that is capable of detecting several attacks. This article discusses all IDS design steps, starting from data collection to the feature engineering analysis and building the trained models. Experimental results show that the proposed IDS can detect four different types of attacks that were seen by the machine learning models during the training phase. The IDS can also detect the existence of several other attacks that are not seen by the model and classify them as unknown attack types. The proposed model achieves 99.97% classification accuracy when detecting known attacks and 85% classification accuracy when detecting a new attack type.
Temperature estimation of electric machines using a hybrid model of feed-forward neural and low-order lumped-parameter thermal networks

Accurate temperature estimation of parts of electric machines using a lumped-parameter thermal network (LPTN) requires knowledge of power loss and thermal parameters. However, modeling parameter varying model inputs (power loss for each node and convective thermal conductances) based on physical equations using only measurement signals available during normal drive operation as inputs is not tangible. Hence, a black-box feed-forward neural network (FFNN) based model is introduced as part of the low-order LPTN which is parametrized using empirical measurements dataset using a newly introduced step-by-step system identification instead of a global identification. Then, the proposed hybrid model performance is evaluated using three different unseen cross-validation data sets, which granted an average maximum absolute error of 5 K.
UAV-Assisted Data Rate Maximization Under 3-D Channel Model

This paper investigates a UAV-enabled wireless downlink system, where a UAV-enabled base station flies above a group of Internet of things devices (IoTDs) and transmits data to them. In order to ensure the fairness among all IoTDs, we jointly optimize scheduling association and UAV trajectory to maximize the minimal throughput of all IoTDs under the 3dimensional (3-D) channel model. Meanwhile, to ensure the stability of communication, we aim to guarantee a high probability of line-of-sight (LoS) links between the UAV and all IoTDs. The optimization problem is non-convex and we propose an iterative algorithm based on the block coordinate descent and successive convex approximation to solve it. Furthermore, we show the convergence of the algorithm through simulations. It is shown that the proposed algorithm achieves higher data rate than the traditional scheme without guaranteeing the LoS probability.
Early Detection of Skin Tumor Using Dipole Antenna

In this paper a dipole antenna is designed with a working frequency of 2.4 GHz. This antenna is used to detect the skin tumor in early stage. The skin is one of the organs of the body which shields us from microbes and it controls our internal heat levels. There are many skin sicknesses which might be brought about by genetic conditions sensitivities and weakened insusceptible frameworks. Dermatology is the part of medicine that manages the conditions of skin. In this paper a dipole antenna and skin phantom is designed utilizing CST software. A 3D skin phantom is planned in CST with the permittivity and conductivity esteems to acquire electromagnetic parameters. The detection of tumor can be achieved by measuring the variations in current density of antenna with and without tumor. The obtained Specific Absorption Rate (SAR) value is 1.32953 W/Kg and obtained values of electric field, magnetic field, current density without tumor is 34485.3 V/m, 39.1352 A/m and 118.287 A/m^2, while obtained value of antenna with tumor is 34664.5 V/m, 39.6489 A/m and 177.842 A/m^2.
A Microwave Biosensor for Aqueous Glucose Solution with Improved-Sensitivity

This paper proposes a microwave sensor with two tunable status by using active element PIN diodes as on-off switches. In the design, two metamaterial units are utilized and two PIN diodes are placed on each unit, thus, four tunable states (I.e. 0000 state and 1111 state), can be obtained. The distribution of the strongest electric field in 1111 state is larger than that in 0000 state, so the sensitivity in 1111 state is higher than the latter. We mold water-glucose mixture solutions into HFSS simulations with relative dielectric constants as 55.7 and 45.9, which indicates glucose concentration of normal people and diabetic patients, respectively. Simulated results show that the sensor with shorting PIN diodes possesses sensitivity of 2-times higher than that of opening case. The proposed sensor can be widely used in the microwave biosensors field to improve the sensitivity of detection.
UAVouch: A Secure Identity and Location Validation Scheme for UAV-Networks

Emerging surveillance applications of UAV teams rely on secure communication to exchange information, coordinate their movements, and fulfill mission objectives. Protecting the network by identifying malicious nodes that are trying to disturb the system is an important task, particularly in the military domain. This paper presents the design and evaluation of UAVouch, an identity and location validation scheme that combines a public-key based authentication mechanism with a movement plausibility check for groups of UAVs. The key idea of UAVouch is to supplement the authentication mechanism by periodically checking the plausibility of the locations of neighboring UAVs, allowing the detection of intruders that are unable to follow expected trajectories. The proposed solution was evaluated in a simulated military surveillance scenario in which it detected malicious nodes’ position falsification attacks with an average accuracy of above 85%.
Failure Characteristics of FPGA-Based Convolutional Neural Networks under RF Interference

FPGAs have been widely used in multiple parallel computing of convolutional neural networks and realizing the parallel acceleration of multiplication, benefitting from its tremendous scalability and flexibility. So far, most of the researches is based on the design and optimization of FPGA-based convolutional neural network accelerators, which lacks researches on the electromagnetic immunity of convolutional neural network accelerators. Therefore, this article mainly studies the failure characteristics of FPGA-based convolutional neural networks accelerators under RF (Radio Frequency) interference. This paper implements two different convolutional neural networks LetNet-5 and YOLO based on a self-made FPGA test board. RF interference is injected into the core pins of the FPGA through a method which is similar to DPI (Direct Power Injection). Next, we observe the failure phenomenon and study the failure characteristics. It is found that under electromagnetic interference, an unusual phenomenon occurred. The research in this paper can provide a reference for the electromagnetic compatibility design of neural network accelerators.
Improving Computational Efficiency of Communication for Omniscience and Successive Omniscience

Communication for omniscience (CO) refers to the problem where the users in a finite set <inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> observe a discrete multiple random source and want to exchange data over broadcast channels to reach omniscience, the state where everyone recovers the entire source. This paper studies how to improve the computational complexity for the problem of minimizing the sum-rate for attaining omniscience in <inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula>. While the existing algorithms rely on the submodular function minimization (SFM) techniques and complete in <inline-formula> <tex-math notation="LaTeX">$O(|V|^{2} \cdot \text {SFM} (|V|)$ </tex-math></inline-formula> time, we prove the strict strong map property of the nesting SFM problem. We propose a parametric (PAR) algorithm that utilizes the parametric SFM techniques and reduces the complexity to <inline-formula> <tex-math notation="LaTeX">$O(|V| \cdot \text {SFM} (|V|)$ </tex-math></inline-formula>. We propose efficient solutions to the successive omniscience (SO): attaining omniscience successively in user subsets. We first focus on how to determine a complimentary subset <inline-formula> <tex-math notation="LaTeX">$X_{*}\subsetneq V$ </tex-math></inline-formula> in the existing two-stage SO such that if the local omniscience in <inline-formula> <tex-math notation="LaTeX">$X_{*}$ </tex-math></inline-formula> is reached first, the global omniscience whereafter can still be attained with the minimum sum-rate. It is shown that such a subset can be extracted at one of the iterations of the PAR algorithm. We then propose a novel multi-stage SO strategy: a nesting sequence of complimentary user subsets <inline-formula> <tex-math notation="LaTeX">$X_{*}^{(1)} \subsetneq \dotsc \subsetneq X_{*}^{(K)} = V$ </tex-math></inline-formula>, the omniscience in which is attained progressively by the monotonic rate vectors <inline-formula> <tex-math notation="LaTeX">$\mathbf {r}_{V}^{(1)} \leq \dotsc \leq \mathbf {r} _{V}^{(K)}$ </tex-math></inline-formula>. We propose algorithms to obtain this <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-stage SO from the returned results by the PAR algorithm. The run time of these algorithms is the same as the PAR algorithm.
Personalized Human Activity Recognition using Hypergraph Learning with Fusion Features

Human activity recognition (HAR) is a promising field which has a wide range of applications in medicine, electronic forensics and Internet of Things. Until now, existing works generally focus on artificial extraction of statistical features or applying deep learning to extract deep features to perform activity recognition. However, most studies neglect individual differences among different users, which leads to performance decline of the trained model when applied to new users. In this paper, we propose a novel approach using hypergraph learning for personalized human activity recognition based on fusion features. Fusion features take advantage of deep features and statistical features, and also fuse the user’s personalization factors to reduce the influence of individual differences. In the classification part, a hypergraph learning algorithm is used to recognize user’s activities based on the fusion features. Experiments on the public dataset USC-HAD (11-class) and the self-collected dataset (6-class) show the proposed method has superior performance compared to existing methods, as well as greater potential for usage in personalized human activity recognition during daily lives.
Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis

Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters challenges such as highly skewed classes, high dimensional feature vectors and highly sparse data. In this study, we have analyzed the improvement achieved by successively addressing these problems in order to determine their severity for sentiment analysis of tweets. Firstly, we prepared a comprehensive data set consisting of Urdu Tweets for sentiment analysis-based hate speech detection. To improve the performance of the sentiment classifier, we employed dynamic stop words filtering, Variable Global Feature Selection Scheme (VGFSS) and Synthetic Minority Optimization Technique (SMOTE) to handle the sparsity, dimensionality and class imbalance problems respectively. We used two machine learning algorithms i.e., Support Vector Machines (SVM) and Multinomial Naïve Bayes’ (MNB) for investigating performance in our experiments. Our results show that addressing class skew along with alleviating the high dimensionality problem brings about the maximum improvement in the overall performance of the sentiment analysis-based hate speech detection.
The Effect Of Sound On The Gyroscopes In Your Car

Motion control systems, such as traditional Electronic Stability Control (ESC) systems, in autonomous vehicles rely on sensors. One of these sensor types are gyroscopes. In our research we found a resonance frequency of an automotive grade MEMS gyroscope when exposing the gyroscope to audible sound frequencies. As this caused the yaw rate to be altered we further analyzed the ESC system’s reaction on an altered yaw rate. This caused a system error that was indicated by error codes and an active warning light. Last but not least we describe improvements that can enhance the ESC system’s availability.
Polymer Optical Fiber-Embedded Force Sensor System for Assistive Devices With Dynamic Compensation

This paper presents the development and the performance analysis of a force sensor using a novel high stretchable polymer optical fiber (POF) fabricated using the light polymerization spinning (LPS) fiber. The system consists of an LPS-POF fiber encapsulated in a flexible material polydimethylsiloxane (PDMS), in addition to a light source and a photodetector. The sensor was characterized by using a commercial 3-axis force sensor K3D60a ±500N/VA (ME Systeme, Germany). Since the LPS-POF and PDMS are viscoelastic materials, tests with loading and unloading cycles were performed to evaluate the sensor response. A viscoelasticity compensation model was proposed to decrease the errors and the sensor phase delay provoked by the viscoelastic behavior. In addition, the LPS-POF force sensor was applied on two different applications, as a gait perturbation system for balance assessment and as a walking cane for gait assistance. Results showed that proposed sensor presents a linear response, with determinant coefficient ( ${R}^{2}$ ) of 0.9974, and high sensitivity ( ${S}=40{N}/{V}$ ). However, the unload time presented high phase delay and errors, corroborating the viscoelastic behavior. Compensated response presented lower hysteresis, leading to a decrease of the root mean square error (RMSE) of approximately 65%. Moreover, numerical integration was used as performance metric for the results of both applications and presented a decrease of 48% in the first application and up to 46% in the second application when used viscoelasticity compensation. The proposed sensor is small and versatile for different applications, and presents simple fabrication, data acquisition and processing.
An Automatic Lung Cancer Detection and Classification (ALCDC) System Using Convolutional Neural Network

The early detection of lung cancer in humans is of significant importance due to its impacts on an individual. The detection of the lung cancer tumor at its early stage is also a challenging problem. Early identification of tumor has the potential to help in saving a large number of human lives. An automatic lung cancer detection and classification (ALCDC) system based on computed tomography (CT) scan images is effective, but the design of a robust lung cancer detection and classification system is a challenging problem. The existing designs of lung cancer detection and classification systems are based on hand-engineered techniques and their outcomes in terms of accuracy and other performance measures are limited. Driven by the exceptional deep learning (DL) success in several recognition related tasks, an ALCDC system based on CT scan images using DL is introduced. For this purpose, using the convolutional neural network (CNN) model, an ALCDC system is built to detect and classify whether the tumors found in the lungs are malignant or benign. The robustness and effectiveness of the proposed ALCDC system is validated using images from the Lung Image Database Consortium (LIDC) and the Image Database Resource Initiative (IDRI). The results indicate that the proposed ALCDC system gives an accuracy of 97.2%. The comparison shows that the proposed ALCDC system performs better than the existing state-of-the-art systems. The proposed ALCDC will be helpful in medical diagnosis research and health care systems.