Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Robustness of Persian Spoken Isolated Digit Recognition based on LSTM

Speech-based interaction between human and machines is one of the most significant progresses in recent years. Digits are one of the aspects of this interaction used in various systems such as voice call systems, bank transaction systems and etc. One of the major problems in spoken digit recognition applications such as all other speech recognition applications is degrading the true detection rate of spoken digits in noisy conditions. Although there are a lot of researches in the field of Persian spoken digit recognition, but there isn’t any significant work in noisy conditions. Therefore, the main goal of this paper is choosing a robust isolated digit recognition approach for Persian language. The proposed method is based on Long Short-Term Memory (LSTM) which is compared with a Hidden Markov Model (HMM)-based method (which is the most common method in the field of Persian spoken digit recognition). The experimental results showed that the LSTM-based spoken digit recognizer is in average 18% more robust than the HMM-based approach in all kinds of additive noisy conditions (five different noises and four different Signal to Noise Ratio (SNR)s for each of them).
High sensitivity hydrogen sensors based on GaN

High quality graphite based Schottky diodes are presented. Schottky barriers were prepared by mechanical deposition of colloidal graphite on GaN substrates uncoverd and partly covered with Pt nanoparticles deposited by electrophoretic techniques. The electron transport for both diodes can be well described by thermionic emission. Hydrogen sensing characteristics of graphite-Pt/GaN Schottky diodes were investigated. Temperature dependence of the sensitivity, the barrier height variation, the ideality factor and the barrier height itself were studied. The proposed hydrogen sensor responds well to various hydrogen containing gases. The room-temperature sensitivity response of 2x107 was obtained in 1000 ppm H2/N2ambient. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
A Seamless Indoor and Outdoor Low-cost Integrated Navigation System Based on LIDAR/GPS/INS

This paper presents an integrated navigation algorithm based on Kalman filter in underground garage. Traditional vehicular navigation algorithms are mainly employed in passenger vehicles traveling on the ground. Vehicles are generally equipped with Global Positioning System(GPS) and Inertial Navigation System(INS) to provide accurate positioning data for vehicles. However, the GPS receiver cannot acquire the positioning signal from the satellite, due to the ground barrier, after the vehicle enters the underground garage. The GPS/Inertial Navigation System(INS) integrated navigation cannot work normally. Inertial navigation can only calculate the position data in a short time, and the positioning accuracy diverges after a period of time. In this case, this paper introduces the integrated navigation algorithm of Light Detection and Ranging(LIDAR) scanner and inertial device. The LIDAR/INS integrated navigation will take place the indoor Dead Reckoning(DR) algorithm. The accuracy and stability of LIDAR scanner indoor navigation and positioning provide calibrated positioning for inertial navigation in Kalman filter. Finally, continuous indoor and outdoor seamless vehicle navigation driving trajectories are achieved by the navigation system.
A Digitally Tuned Flexible Reconfigurable Antenna for IoT Devices

In this paper, a digitally tuned flexible antenna is proposed for integration onto devices within the Internet of Things (IoT) paradigm. The antenna design caters for the various IoT communication standards by reconfiguring its operating frequency. Accordingly, the antenna reconfiguration allows operation at 868 MHz, 915 MHz, 1.8 GHz and 2.1 GHz. The reconfiguration is achieved digitally by resorting to a digitally tunable capacitor (DTC). The DTC changes its capacitance, and hence its frequency operation, by relying on 5-bit sequence commands through a microcontroller. The proposed antenna is coplanar waveguide fed with its ground plane surrounding the radiating structure. The gain of the antenna varies between 4 dB and 6 dB for different frequencies of operation. Its radiation efficiency varies between 78% and 92%. The antenna is fabricated and tested on a flexible substrate, where the resulting reconfigurable prototype maintains its performance characteristics for different bent states.
Quantitative Analysis of Flavonoids in Annona squamosa Leaf Extracts and Its Pellet Formulation by Validated High-Performance Thin-Layer Chromatographic Technique

Annona squamosa, commonly known as custard apple, possesses various medicinal properties such as antimicrobial, insecticidal, cardiotonic, etc. Leaves of A. squamosa are known to contain various types of flavonoids which are responsible for these activities. The aim of this article was to develop and validate a high-performance thin-layer chromatography (HPTLC) method for the determination of rutin and isoquercitrin in the extracts of A. squamosa and in an in-house developed herbal pharmaceutical formulation. Analysis was performed on TLC aluminum plates precoated with silica gel 60 F254 as the stationary phase. Linear ascending development was carried out in a twin-trough glass chamber saturated with a mobile phase consisting of ethyl acetate-formic acid-glacial acetic acid-ethyl methyl ketone-water 50:7:3:30:10 (v/v/v/v/v) at room temperature (25 ± 2°C). A CAMAG TLC scanner III was used for the spectrodensitometric scanning at 366 nm. The system was found to give compact bands for rutin and isoquercitrin (RF values of 0.32 ± 0.05 and 0.59 ± 0.03, respectively). The detector response was linear for concentrations ranging between 200 and 1600 ng per band (r2 = 0.9885 ± 0.0041 for rutin and 0.9828 ± 0.0013 for isoquercitrin). The limits of detection and quantitation were 75 and 100 ng per band for rutin, and 40 and 80 ng per band for isoquercitrin, respectively. The recovery study was carried out by the standard addition method and was found to be 96–107%. The present method was found to be simple, specific, sensitive, and accurate and was used successfully to quantitate rutin and isoquercitrin in the leaf extracts of A. squamosa collected from five different locations of India and in a pellet formulation of the extract.
Research on Wind Power Optimal Dispatch Based on Model Predictive Control

As high penetration of wind power integrated in-to the large-scale power grid via the transmission line, active power control is prone to poor tracking control construction and over the capacity of the transmission line, and it is difficult to effectively suppress the power fluctuation caused by the randomness of wind power in traditional control methods. Therefore, a novel wind power optimal dispatch based on model predictive control (MPC) is proposed. In this approach, a combined prediction model based on the variancecovariance (VC) variable weight is adopted to provide prediction information for dispatch. To maximize wind power output, a finite time domain rolling optimization and real-time feedback correction of wind power strategy is developed for intraday dispatch, and in the feedback correction procedure, the current output state of the wind power is taken as the initial value of the next round of receding horizon optimization dispatch. A case study is carried out with a regional wind farm in North China taking as an example. The simulation and experimental result analysis show that the control strategy proposed in this paper can not only suppress the power fluctuation, but also effectively maintain the safety of the transmission line.
Sentiment Analysis of Russian IRA Troll Messages on Twitter during US Presidential Elections of 2016

In this paper we evaluate the sentiment of messages by Russian Internet Research Agency (IRA) on Twitter discourse during US Presidential Elections of 2016 using VADER-a rule-based model for sentiment analysis of social media text. We use two datasets for analysis. The first consists of 51.3 million tweets collected during the US Elections of 2016 (October 30th, 2016-November 18th, 2016) and the second was shared by Twitter in October 2018, consisting of 8.77 million tweets generated by IRA accounts over a decade. We look for overlap of IRA tweets in the two datasets, evaluate their sentiment, and compare it with sentiment of other messages during that time period discussing the two Presidential candidates. Our findings show: (1) IRA tweets and retweets had a significantly positive sentiment towards Donald Trump and negative sentiment towards Hillary Clinton; (2) IRA messages mentioning Hillary Clinton had a more negative sentiment than non-IRA messages in our dataset.
Research on the Optimization Method of Capacity Configuration of DC Microgrid

For the uncertainty brought by the connection of new energy to the grid, the research on the optimization of the capacity allocation of the DC microgrid becomes very important. The optimal configuration of DC micro-grid is the key to ensure the stable and economic operation of microgrid. This paper takes multi-layer optimization as the main idea to optimize the capacity configuration of the micro-grid. Firstly, the economic optimal solution of the system is obtained by enumeration method. With the lowest total cost of the project as the objective function, and the sensitivity analysis conditions of diesel price and new energy efficiency are taken as the sensitivity analysis conditions to obtain the economic optimal scheme and conduct preliminary analysis. Secondly, by taking the first-layer optimization results as the initial solution, particle swarm algorithm (PSO) is adopted to calculate and find the optimal scheme of system performance by taking economic conditions, new energy utilization rate as the constraint conditions, and the minimum sum of load power shortage rate and energy surplus rate as the objective function. The multiobjective problem is transformed into a single-objective problem by the weighted average method, and used to obtain the optimal solution. Finally, droop control method is introduced for the system to verify the stability of the system. This shows that the system can maintain power balance and provide correct guidance for stand-alone DC microgrid capacity design.
Wavelet Scattering Based Gated Recurrent Units for Binaural Acoustic Scenes Classification

A Gated Recurrent Units (GRUs) for Acoustic Scenes Classification (ASC) with wavelet time scattering is proposed. The neural networks using a combination of multiple GRU layers in the Neural Network enhance ASC’s accuracy. Due to the advantage of time sequence modeling, the GRUs can perform better class-based discrimination and mapping features. The results on the TUT Acoustic Scenes evaluation dataset demonstrates that the proposed model performs better in ASC than the bidirectional Long short-term memory (LSTM) layer and its counterpart LSTM. The average class-wise accuracy has shown around 23.7% improvement with the proposed GRUs network. Compared to other highest performance LSTMs based method, GRUs offer 3.6% and 4.8% improvement in the classification accuracy and F1-Score, respectively.
A Methodology to Handle Spectral Smearing in Gearboxes Using Adaptive Mode Decomposition and Dynamic Time Warping

Tacho-less order tracking is an effective tool for fault detection in gearboxes operating under speed fluctuations. This technique’s performance depends on extracting instantaneous shaft speed information from a complex multicomponent gearbox signal, which is first preprocessed by a bandpass filter. However, spectral overlap resulting from the time-varying frequency components makes it challenging to isolate the shaft speed harmonic mono-component using this approach. In order to overcome such issues, an adaptive tacho-less order-tracking (OT) method combining the variational mode decomposition (VMD) and the fast dynamic time warping (FDTW) is proposed in this article. The proposed method first decomposes the measured gearbox vibration signal using VMD to estimate the instantaneous shaft speed profile to construct the shaft vibration signal. The gearbox vibration signal is then resampled based on the optimal warping path obtained by FDTW, which performs an “elastic” stretching and compression along the time axis of the extracted shaft vibration signal with respect to a sinusoidal reference signal with a constant shaft rotational frequency. Finally, the presence of gear fault is detected by constructing the order spectrum of the resampled vibration signal. The effectiveness of the proposed algorithm is demonstrated using both simulation analysis and experimental validation using measurements from two different wind-turbine gearboxes collected under test and field conditions, respectively.
Supervised Adaptive-RPN Network for Object Detection in Remote Sensing Images

Object detection is one of the most important tasks in the field of very high resolution (VHR) remote sensing (RS) images understanding. Due to the characteristics of VHR RS images, the detection performance is always limited by class imbalance and intersection-over-union (IoU) distribution imbalance. To mitigate the adverse effects caused thereby, we propose a supervised adaptive-RPN (SA-RPN) model with the help of deep learning in this paper. First, we introduced a supervised multi-dimensional attention network to overcome the foreground-background class imbalance. It can help the network to highlight the foreground and suppress the background effectively. Second, we develop the adaptive-RPN to reduce the negative impact of the IoU distribution imbalance by adaptively selecting the size of the anchor. The positive experimental results on the public data set validate the usefulness of our SA-RPN model. Compared with the popular deep learning RS object detection methods, our method achieves improved performance.
Dense Greenhouse Extraction in High Spatial Resolution Remote Sensing Imagery

Greenhouses are densely distributed across the cultivated land in high-resolution remote sensing imagery, resulting in the problem of dense object extraction. On the one hand, objects tend to be wrongly merged into one object since objects are connected; on the other hand, the existed random sampling scheme does not make good use of the object distribution density to improve the training effect. To meet the demand for dense greenhouse extraction, this paper proposes a novel deep learning-based greenhouse extraction algorithm. To solve the problem that dense objects tend to be wrongly merged, this paper proposes a dual-task learning module, which uses the discriminant object boundary of the pixel-based branch to distinguish the objects of the object-based branch; to take advantage of the object distribution density for effective training, a high-density biased sampler is proposed. Moreover, this paper provides a dataset of manually labeled imagery to train and develop the proposed method. Results on greenhouse extraction in six regions in China achieve peak mIoU and mAP value, surpassing the state-of-the-art methods. Finally, a product of a greenhouse map in China is provided for analysis.
Probability Fusion for Hyperspectral and LiDAR Data

In this paper, a new probability fusion strategy is proposed for hyperspectral and LiDAR data classification, which is inspired by the representation residual fusion strategy in our previous work. Unlike the residual fusion strategy utilizes a collaborative representation classifier, the probability fusion strategy deploys a deep residual network (DRN). This paper compares the two fusion strategies. The experiment results show that the probability fusion strategy with DRN is better than the residual fusion strategy in classification performance.
ESA-Based Anomaly Detection of a Centrifugal Pump Using Self-Organizing Map

Centrifugal pumps are among the most common equipment that are used in different industries. The lack of timely diagnosis of a present fault may disrupt operation of the industrial plant and impose considerable expenses. Thus, the need for a cost-effective fault detection method of the equipment is undeniable. In this research, a data-driven anomaly detection approach is proposed to diagnose some common faults in centrifugal pumps using only electrical signals. A particular type of neural networks, Self-Organizing Map (SOM), is utilized to learn healthy condition and detect three introduced faults and inefficient operating points in test phase. The experimental results show the effectiveness of the proposed method in detecting anomalies in centrifugal pumps.
IoT Botnet detection based on the integration of static and dynamic vector features

The Internet of Things (IoT) provides the infrastructure for global social connectivity, where everything is connected, and information exchanged over the Internet. Because of this peculiar, the amount of botnet malware infecting IoT devices to carry out attacks such as DDoS, cryptocurrency malicious mining, keylogger, etc., is expanding at a fast pace. Therefore, detecting the IoT botnet is essential. Most IoT botnet detection approaches use either static analysis features or dynamic analysis features, and rarely integrate them as detection features, also no more work is consumed integrating the two types of features. In this paper, we propose a first method that utilizes the integration of both static and dynamic features with machine learning classifiers for distinguishing IoT botnet from benign samples. Experiments show that our proposed method can obtain an accuracy more than 99% on a total of 6520 samples with 4644 IoT botnet samples. Besides, we show that using integrated features for IoT botnet detection outperforms using static feature or dynamic feature alone, and other studies.
A Comparative Study of Machine Learning Approaches for Recommending University Faculty

Academic program selection is a vital decision as it plays a substantial role in enhancing opportunities in career paths. Numerous researches utilizing psychometrics have been conducted in recent years, and result reveals an evident association between academic fields and personality traits of students. To assist students in making an informed academic decision, this study analyzes the differences amongst the students of nine study programs in Bangladesh and envisages the appropriate academic choice among these nine fields for the prospective students by analyzing their academic grades, personality traits, and intelligence. For the experiment, we have collected data from 103 participants and used students’ academic grades, Big-5 personality traits, and Factor B (Reasoning) of 16PF to develop classification models by utilizing two classification approaches. Results show that the Hierarchical classification approach using Random Forest Classifier outperformed the One-level Random Forest classifier approach by obtaining 96.1% accuracy for the 1st-level and 92.86%, 89.29%, and 94.74% respectively for 2nd-level. The findings of this study demonstrate that the proposed framework has great potential in assisting prospective students to make an informed decision to determine the suitable higher study options while unlocking human potential.
NC-MAC: Network Coding-based Distributed MAC Protocol for Reliable Beacon Broadcasting in V2X

To support emerging vehicular applications such as autonomous driving, the reliability of beacon broadcasting becomes an indispensable issue which is also difficult given the highly dynamic topology and vehicle density. For the territories which are not covered with communication infrastructure, controlling and scheduling in a centralized way may not be applicable. Therefore, vehicle-to-vehicle (V2V) ad hoc communication is promising to address the ubiquitousness coverage concern. In this paper, a distributed network coding-based medium access control protocol (NC-MAC) is proposed to support V2V beacon broadcasting. We combine the preamble-based feedback mechanism, retransmissions, and network coding together to enhance broadcasting reliability. Extensive simulations are given to show the performance gain of the NC-MAC protocol compared to the existing 5G cellular vehicle-to-everything (C-V2X) MAC protocol in a wide range of scenarios.
A Deep Learning-Based Real-time Seizure Detection System

Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5].
Channel-Quality Reporting Enabled by Machine Learning in Non-Stationary Environments

In this paper, we propose a novel channel quality reporting approach for cellular communication systems. The proposed approach features a differential coding scheme in stationary propagation conditions and a detector of non-stationary propagation conditions, which further triggers a channel-quality predictor for the non-stationary environment based on a machine learning method. In particular, the machine learning engine learns about the specific large variations of the channel quality by collecting signaling information from mobile terminals in a given region. Our simulations in a controlled urban environment with vehicular users show that the proposed solution can effectively replace the 4-bit channel-quality reporting scheme of LTE and NR standards with a 2-bit one, providing correct channel-quality indication in non-stationary conditions with high probability.
Hyperspectral Image Change Detection by Self-Supervised Tensor Network

In multitemporal hyperspectral image change detection (HSI-CD), the high dimensionality of HSIs makes it difficult to extract discriminative features or perform accurate clustering with unsupervised models. Meanwhile, most supervised deep learning methods rely on a large amount of labeled samples which are hard to collect in reality. To address these issues, this paper proposes a Self-Supervised Tensor Network (SSTN) for HSI-CD to obtain more discriminative features for clustering. It regularizes the TN by clustering to automatically obtain discriminative features without human intervention. Experiments on three sets of real-world data show that the proposed method outperforms several state-of-the-art methods for CD.
Fuzzy Adaptive Sliding Mode Controller for A Chaotic System with Uncertainties and Disturbances

In this paper, a fuzzy adaptive sliding mode control (FASMC) technique is used to control a new chaotic system. Using sliding mode control, a unique sliding manifold is considered, and the control law is achieved. The adaptive sliding mode control (ASMC) law is to make the system states track the desired signal and guarantee the property of asymptotical stability. In addition, the designed controller can control the uncertain chaotic features to the desired states stability, it can also reduce the chattering effect noticeably. The simulation results demonstrates the effectiveness and the accuarcy of the proposed control scheme.