Miracle-dz/UniFAR-FTUs huggingface dataset:
Fourier Domain Kernel Density Estimation-based Approach for Hail Sound classification

In this study, a method that recognizes the sound of hail is proposed for a system designed to minimize the damage caused by hail to vehicles. The designed system uses signal processing and machine learning. The sounds received by a microphone in the vehicle were converted into frequency space and the kernel density estimation of the frequency values occurring in a certain time interval (approximately 2 seconds) was obtained. This is based on the prediction that the histogram of the frequency of hail falling on the car can have a defining characteristic. In this context, it has been designed to create a two-class machine learning problem, including full sound samples and ambient sound samples. A solution to the machine learning problem was sought with the Support Vector Machines (SVM) algorithm. The SVM algorithm was chosen due to its simplicity and fast working dynamics. While learning is offline in the SVM algorithm, testing is done online. Related software was implemented using MATLAB. In experimental studies, we collected a dataset where almost 500 hail sound segments were used and similarly 400 ambient sound segments were collected. A hold out cross validation approach with various split ratio values are used. It has been seen that the proposed method predicts hail sounds with 92.22% accuracy when the hold out cross validation ration is 90% and 10%.
Analysis of Drought at Terai Regions in Uttarakhand using Multiple Remotely Sensed Data

With developments in remotely sensed imageries, nowadays satellite images provide more narrow wavelength bands in higher resolution and frequent revisit time such as Sentinel-2. Sentinel-2 can be used to generate Normalized Difference Water Index (NDWI) which explains the water or moisture content of the object of interest. In this study, ERA5-Land total precipitation data and Sentinel-1 SAR C-band were also used to assess drought that happened in Terai Region around Uttarakhand in 2021. Study area is divided into three study sites: Lower Terai, Reservoir, and Hilly Area to accommodate three different landscape condition. The results show that NDWI values fluctuates between 0.25 to -0.05 around croplands but not in the densely vegetated area. Also, the changes in NDWI values around Lower Terai follow the trend in the Hilly Area but not the Reservoir. The results from three remotely sensed data also show that the minimum NDWI value was captured around November to December 2021 in Lower Terai and Hilly Area, while in Reservoir area it was in September. Overall, the three datasets had shown the ability to detect changes in moisture and water in Terai region, Uttarakhand.
Fractional Order Networked Control System using Stochastic Iterative Learning Control

Networked control systems (NCSs) are one of the main areas in control and computer engineering. Despite the many advantages that NCSs have, they impose restrictions such as random data dropout on control systems. We can remove the impact of data dropout by employing the iterative learning control (ILC) method for repetitive systems. In this paper, an ILC for stochastic fractional order NCSs has been presented to deal with the random data dropout imposed by the network. Using the Kalman filter approach, the optimal learning gain is determined so that the system output tracks to the desired output in the presence of random data dropout. The simulation results show that the mean of tracking error becomes close to zero after 500 iterations when a significant number of data packets have been lost in the network.
An Encrypted Abnormal Stream Detection Method Based on Improved Skyline Computation

With the development of a new generation of mobile communication technology and the enhancement of user security awareness, a large amount of data containing private information generated by users every day will be transmitted in an encrypted form in the network, and it is difficult for traditional abnormal stream detection methods to detect encrypted data, which will increase the likelihood of DDoS attacks on servers that store user information. In response to this problem, this paper proposes a method called detection of encrypted abnormal stream based on improved skyline(DEF-IS). First of all, the Order-Revealing Encryption algorithm is used to encrypt the data stream to ensure the security of the data stream; Then, efficient encrypted abnormal data stream detection is carried out based on reservoir sampling algorithm and improved skyline algorithm; Finally, the performance of the DEF-IS algorithm is verified in the simulation environment. The experimental results show that DEF-IS algorithm can quickly and accurately detect abnormal data while ensuring the safety of data.
Spacecraft Resources Dynamic Scheduling Strategy Based on Reinforcement Learning

With the development of intelligent control of spacecraft, spacecraft needs to have the ability of dynamic scheduling of on-board resources. In this paper, reinforcement learning is applied to the dynamic scheduling of spacecraft finite resources. The space situation information under a specific mission is used as the input, and the dynamical spacecraft resource allocation is output. In order to further improve the effectiveness of the algorithm, the classical Deep Q-learning Network (DQN) algorithm is modified. Aiming at the convergence issues of the classical DQN algorithm, the varying epsilon mechanism and normalization are introduced. In view of the limitation of spacecraft resources, the Action-Mask mechanism is introduced. Simulation experiments show that compared with the random scheduling strategy and the scheduling strategy based on expert knowledge, the modified DQN algorithm proposed in this paper ensures the spacecraft to complete the mission more smoothly and saves the velocity increment of the spacecraft.
Visualizable Malware Detection based on Multi-dimension Dynamic Behaviors

For different motives and reasons, malicious software, or malware, has been frequently used to damage and destroy information systems in many various environments. Especially, with the widespread application of the Internet and the reemergence of artificial intelligence, security and privacy are significantly stressed by individuals and businesses, and the influence of malware is being more and more serious in recent years. Unfortunately, the previous traditional malware detection schemes are infeasible in the AI era, as they cannot effectively recognize and identify the unknown or new types of malware, which could be easily and quickly brought out by automatic combination and improvement. Although many AI-based malware detection schemes also have been proposed at the same time, they are suffering from heavy training and low accuracy problems. Therefore, in this paper, we propose a visual malware detection model and concrete scheme based on multi-dimensional dynamic behaviors including all API and network operation information. To train our model, we collected a malware behavior dataset containing over 700 malware-image pairs, where the images are grayscale and converted from the malware execution behaviors recorded in the Cuckoo sandbox. Compared with the existing visual detection schemes, we introduce and employ the malware's dynamic behaviors information to produce the visual images, and we avoid the manual feature extraction in constructing the training dataset. Moreover, our experimental result shows that our scheme can outperform the existing schemes in detection accuracy and efficiency as well as the ability of new malware recognition.
High-precision Path-following Control for Underactuated Vessels via the Improved Dynamic Virtual Ship Guidance

This paper investigates the problem of high-precision path-following and automatic obstacle avoidance for underactuated vessels. An artificial potential field (APF) is introduced into the dynamic virtual ship (DVS) guidance to propose a new guidance scheme to assure the rationality of the obstacle avoidance mechanism, which can automatically plan the planned route according to the route point and use the planned route as the gravitational source. In terms of control, an improved Iterative Learning Control (ILC) algorithm is designed. For engineering implementation, backstepping, neural networks, and dynamic surface control (DSC) techniques are used to reduce the computational effort of the algorithm. The derived controller is with the advantages of high accuracy, low computational effort, and is easy to engineering implementation. The theoretical stability of the closed loop control system was proofed by the Lyapunov theorem. Finally, the feasibility of the scheme is verified by simulation experiments.
Real time Chest X-ray Pathology detection and localization framework with Convolutional Neural Networks and Ensembling

The primary methods of detection of abnormalities in the chest and heart region are analysis of Chest X-rays by expert radiologists trained over multiple years. The cycle time and accuracy for diagnosis with radiographs are often not favorable for the patients leading to undesirable outcomes. To mitigate this issue, we have developed a fully online end-to-end radiology application that performs highly reliable classification and localization of 14 different pathologies using an ensemble of different deep convolutional Neural network architectures and presents it as an editable output for any radiologist to investigate, make changes and confirm the outcome. Our core Deep learning algorithm also achieves a meaningful mAP@40 on VinDr-CXR test set. This is very significant achievement and can help to reduce subjectivity in detection by bringing in more consistency.
Cosmic‐ray particles that changed from 1954 to 1958 to 1965

During each of the two recent periods of special international coordination of scientific effort, the IGY (International Geophysical Year) and the IQSY (International Year of the Quiet Sun), we made a latitude survey of cosmic rays, using ionization chambers carried by balloons. All instruments were carefully compared with the same set of standards. In each case, simultaneous balloon flights were made from a high-latitude base station. Such monitoring of the primary radiation allowed corrections to be made to the data from the mobile station. Ratios of the abundances of the different nuclei to that of protons in the primaries as found by other observers have been used to separate out the effect due only to protons. Two methods are used to find the differential spectrum of the protons for the different years. Above 1 bev, the areas under successive latitude difference curves are used. This method takes advantage of the earth's magnetic field as an analyzer. Below about 0.6 bev the ranges of protons in air and areas are used. The two methods give results that are consistent with each other. Where applicable the results are also in good agreement with the differential spectra of protons found by other observers using satellites and high-altitude balloons. From the integral of the differential spectra so derived for the different years, it is found that the total number of primary protons increased by a factor of 3.1 between 1958 and 1965. From similar flights made during the previous solar minimum the change from 1954 to 1958 is found to be a factor of 4.2. This, as well as solar activity data, indicates that there was more residual modulation of the primary cosmic radiation during the 1965 minimum than was present in 1954.
FPGA Implementation of a Lightweight Convolutional Neural Network Classifier for Speech Emotion Recognition

This paper focuses on how to deploy CNN on FPGA for speech emotion recognition. The features of the speech data are extracted and saved to the SD card on the PC side. The trained network weight matrix is also held on the SD card. We designed the modules of CNN by HLS. FPGA reads the speech features and the network weight matrix from the SD card; then, the deployed CNN model performs the emotion prediction. The results are sent by UART serial port to the PC side. In this paper, the Berlin EmoDB corpus is used for the experiments. We first experiment on the software side and achieved an 85.67% recognition accuracy. Then we get the same accuracy on FPGA verification.
Wet-process preparation of amorphous Y-Fe oxide films ferromagnetic at room temperature

Yttrium iron ferrite films of amorphous structure (as examined by x‐ray diffraction), having ferromagnetic moment at room temperature, are successfully synthesized from an aqueous solution by a ferrite plating method in which the substrate is heated by Xe‐lamp beams. The atomic ratio of Y/Fe in the films is adjusted to Y/Fe=0.6, as in Y3Fe5O12, by properly choosing the atomic ratio (Y/Fe=3.5) in the reaction solution. The magnetization of the films at room temperature is 10–40 emu/cc, several times smaller than that observed for crystalline YIG. It exhibited, however, a paramagnetic Mossbauer spectrum at room temperature, suggesting that the weak magnetization is ascribed to the magnetic impurity phase whose concentration is too small to be discerned by x‐ray diffraction and Mossbauer measurements. The spectrum has an isomer shift of 0.22 mm/sec at 300 K and a hyperfine field splitting of 462 kOe at 12 K, indicating that the Fe ions are in a trivalent high spin state.
Fully Distributed Event-Driven Formation Control Over Directed Information Topologies

This paper studies the formation control problem of networked second-order integrator systems from a distributed event-driven perspective, where the unknown nonlinearity is involved in the system model. An event-driven formation control scheme, which consists of a fully distributed event-driven control protocol and a fully distributed triggering function, is developed in this paper. In order to handle the nonlinear dynamics, the approximation property of the neural-network control is utilized. Adaptive gains instead of constant gains are designed in the control protocol, making the control scheme fully distributed. Using the proposed triggering mechanism, the control torque of each agent is a piecewise constant function, which is updated discontinuously and asynchronously. Moreover, the information topologies among different agents are directed and the prescribed formation configuration is time-varying. These settings are more practical, but brings some difficulties to control scheme design and theoretical analysis. It is shown that under the developed control scheme, each agent can achieve the prescribed formation configuration without causing the undesired Zeno behavior. Finally, numerical simulation is performed to confirm the validity of the main theorems.
Multi-USV Deep Reinforcement Learning for Distributed Cooperative Target Tracking

The purpose of this paper is to discuss distributed cooperative target tracking for a multi-unmanned surface vehicle (multi-USV) system. The cooperative target tracking problem is formulated as a multi-USV learning problem. Based on this formulation, a multi-USV distributed cooperative target tracking (MUTT) algorithm is proposed. To avoid the collisions between USVs during the tracking process, an additional safety layer is introduced. Some safety signals are constructed based on USVs' states. By correcting actions through the trained safety layer, USVs can avoid collisions reasonably. Moreover, for the sake of demonstrating the effectiveness of the proposed MUTT algorithm in target tracking, reward functions and mission scenarios are well constructed. Furthermore, a comparison of the MUTT algorithm and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is given. The obtained results manifest that the proposed MUTT algorithm provides safe policies for multi-USV cooperative target tracking tasks.
Effects of Structure Shape Optimizing Techniques on the Performance of a Large Scale Wound Rotor Synchronous Machine

Wound rotor synchronous machines with fractional slot non overlapping windings are attractive for low speed, grid connected wind applications. As they contain no magnets and provide ample flux variation, with the added advantages of the fractional slot non overlapping winding they are a very cost effective solution for this kind of application. The main drawback of this kind of winding is high harmonic content in the air gap magneto-motive force waveform, which is evident by the presence of high torque ripples and increased rotor core losses leading to a depreciated overall machine performance. This paper seeks to study the effects of pole placed flux barriers and pole shaping techniques on the performance characteristics of a large scale wound rotor synchronous machine. To perform such a task, a 3MW, 400V, 16 pole, 18 slots, double layer wound rotor synchronous machine is designed and analyzed using a python based in-house static, 2D time stepped finite element method called SEMFEM and verified in the commercial transient finite element method software Ansys Maxwell.
A hierarchical based ensemble classifier for behavioral malware detection using machine learning

Malwares are increasingly threatening the security and confidentiality of data. Therefore, the issues related to malware detection are gaining interest among the researchers. In this research work, an efficient behavioral malware detection system has been proposed for Portable Executable (PE) files. The detection of malware is done through machine learning classifiers. The most recently published dataset (containing samples from august 2019 to September 2020) namely, Blue Hexagon Open Dataset for Malware Analysis (BODMAS) has been used to train and test the proposed design. The proposed methodology is divided into two stages. First stage contains a binary classifier which detects whether PE file is malicious. A random forest is used as a binary classifier for this stage. Second stage contains a multi-class ensemble base voting classifier, and it detects the family of malware. K-nearest neighbor (KNN), support vector machine (SVM), random forest, decision tree and gradient boosting are used in voting classifier with equal weights. The proposed methodology achieved significant results with 99.48% accuracy in the first stage (binary classifier) and 92.49% accuracy in the second stage (ensemble-based classifier) on BODMAS dataset.
Analysis of SIW Phase Shifter Types with Mode Matching Technique (MMT)

This paper uses Mode Matching Technique (MMT) to study and analyze three types of Substrate Integrated Waveguide (SIW) phase shifters. Three design equations are presented and used to design three-phase shifter prototypes. The designed phase shifters are analyzed with MMT and full-wave simulator CST and the results are compared and discussed. The cascading procedure in SIW structure is achieved by the help of Generalized Scattering Matrix Method (GSMM).
Adaptation of a Research-based Teaching-Learning Format with Approaches of Online Learning in the STEM Field

Research-based learning (RBL) familiarizes students with the academic research process at an early stage and at the same time offers them the opportunity to actively shape their own learning process. Working on their own research problems allows students to go through the entire research cycle, which promotes not only subject and methodological competencies but also the students' self-competencies. The handling of subject-specific and didactic challenges of this form of teaching-learning has been discussed many times in the past. New challenges for research-based learning now arise from the shift of teaching and learning to virtual space associated with the COVID-19 pandemic. This paper focuses on the adaptation of a research-based teaching-learning format to the demands of e-learning in the course Interaction Science with Artificial Intelligence. We evaluated the adapted teaching-learning format with 18 students in a master's program in STEM. The students stated an increase in professional competence in the areas of programming, data preparation and data visualization. Our results suggest that peer group and direct interaction via synchronous communication channels are important structural frameworks for research-based learning in an online learning context. From our results, we can derive initial implications for online-based RBL in the field of computational education.
On Thermal Image Classification with ANN and Similarity Based Classifiers

The objective of the work is to evaluate a set of classifiers based on similarity of patterns, in the context of human thermal images, and for face and emotion recognition. The processing and classification of such images is important for various difficult environments, i.e., media with less lightning, including here the actual pandemic context. The classifiers use various distances to evaluate the similarity among patterns. The classification objective is to recognize the person and the state, from a set of three values, i.e., normal, happy, and sad. Three solutions were considered. The first one uses an artificial neural net, in a standard multilayer feedforward structure. The second one uses the discrete cosine transform (DCT) for feature selection. The last one uses the entropy of the processed images for feature selection. A classifier based on minimum distance is used for the last two solutions. The best results are obtained with DCT based features. The methods could be extended to other types of images, as ultrasound or time-frequency based images.
Intelligent Bearing Fault Diagnosis With a Lightweight Neural Network

Bearing fault diagnosis plays a vital role in machine health monitoring. With the support of deep learning methods, bearing fault diagnosis based on neural networks using vibration signals has achieved excellent results. Compact models with high accuracy that can be embedded in handheld devices are in high demand. This research proposes a novel method for bearing fault diagnosis based on vibration signals using a low-computation-cost deep learning model. Generally, the vibration signal collected from the bearing of the electrical motor is transformed into the spectrogram images by Constant-Q nonstationary Gabor transform, in which these images are used as the training data. Subsequently, the proposed method uses a compact convolutional neural network, called MobileNetV3, combined with AutoCompress pruning method to decrease the number of required parameters and multiply-accumulate operations. Experimental results manifest that the nominated method gains an accuracy of up to 99.34%. Compared with other deep learning models with similar accuracy, the proposed model has a 2-times less number of parameters and approximately 8-times less number of multiply-accumulate operations.
Covid-19 Chest Radiography Images Analysis Based on Integration of Image Preprocess and Ensemble Learning Model

COVID-19, a highly insidious infectious disease, is now widespread in the world and has caused hundreds of millions of dollars of damage. Rapid detection for COVID-19 is essential for outbreak control, yet existing RT-PCR assays require hours to obtain results and may be incorrect. To enable faster and more accurate detection of COVID-19 infected patients, training neural networks on chest X-ray scans and using the trained models to assist in the diagnosis of lung disease is worth considering. Our team constructed a model consisting of 5 CNN networks of AlexNet, VGG11, GoogleNet, ResNet18 and DenseNet121 with a fully connected neural network using transfer learning and ensemble learning. This model is able to combine the advantages of the 5 CNN networks to get better results. At the same time, we use CLAHE image enhancement algorithm with image augmentation to optimize the training set, which avoids overfitting problem and can further improve the results. With the above approach, we can train a highly accurate ensemble model in a short time to quickly detect COVID-19 infected patients with a small sample of chest X-ray images. Our ensemble model converges quickly and the final test accuracy is 96.48%, which is higher than the test accuracy of any of the five individual CNN networks.