Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictability and Analysis of Weekly Temperature using Autoregressive Time Series Models

Analysis and forecasting of time series have become a major tool for analyzing, forecasting and in other applications in various meteorological and hydrological data like temperature, rainfall, humidity, river flow, stream flow and many more others. In this paper our goal is to forecast the future trend of highest and lowest temperature of Punjab, using non-seasonal and Seasonal autoregressive moving average and Seasonal autoregressive integrated moving average models. For this we collect and analyze the past values and developed a suitable model which describes the inherent structure and characteristics of our series. We calculated the accuracy of our results on the basis of forecast error measures and hypothesis testing. The forecast performances such as mean absolute error, median absolute error, Root mean square error and normalized root mean square error are used for the temperature of Punjab. Forecast results obtained by us in the paper will be useful for various agencies for planning and development of various meteorological and hydrological decisions.
Fault diagnosis of bearing using Deep Neural Network with Dropconnect

Fault diagnosis of a rotating machine is one of the most important tasks from the perspective of system maintenance and repair. Recently, machine learning has been widely used as a fault diagnosis method for rotation machines. However, the algorithm has an overfitting problem. To solve this overfitting problem, this study proposes a fault diagnosis method for rotating machines by applying DropConnect. Useful features were extracted from vibration signals for fault detection and applied to Deep Neural Network(DNN) models.
A Fault Prediction Method Based on IAALO-SVM and Similarity Measure

As an important part of electronic products, analog circuits are widely used in various fields. However, the fault prediction technology of analog circuit is still in its infancy because of its nonlinear characteristics such as nonlinearity and tolerance. According to the characteristics of small sample nonlinear data in analog circuits, the farther the prediction time distance of IAALO-SVM algorithm is from the initial training sample, the greater the prediction error is. Therefore, this article through the use of analog circuit fault parameters offline database, introduced the choice of time series similarity measure method is similar to database, according to the size of the similarity for failure prediction result, combining the measured data and offline data is put forward based on IAALO - SVM and similarity measure the failure prediction of new scheme to predict the residual service life of main amplifier circuit. The example shows that the new scheme can obtain high prediction accuracy in a long time and has practical application value.
A Compact Soft MMSE detection Scheme for Uplink Coded Massive MIMO Systems

Joint iterative detection and decoding (JIDD) methods with minimum mean-squared-error parallel interference cancellation (MMSE-PIC) are usually used for signal detection in multi-input multi-output (MIMO) systems. However, the computational complexity caused by computation of Gram matrix and matrix inversion is unbearable for massive MIMO systems. In addition, MMSE-PIC also suffers from high complexity in calculating soft bit information (SBI) and post-equalization signal-to-interference-plus-noise ratio (PE-SINR). In this paper, we propose a complexity reduced soft MMSE-PIC detection scheme by approximating the Gram matrix with a derived diagonal matrix. Besides, we also apply an efficient soft symbol estimation method based on a symbol mapping technique to further reduce the computational complexity. The simulation results demonstrate that the proposed method can achieve approximating performance to the conventional method with linear-order computational complexity.
Influence of Winding Distribution on Fault Tolerant Performance in a Fault-Tolerant Permanent Magnet Rim Driven Motor

The Fault-Tolerant Permanent Magnet Rim Driven Motor (FTPM-RDM) is a high performance, high reliability marine driven motor. The short-circuit current and the mutual inductance voltage generated by the short-circuit current are important parameters to indicate the fault-tolerant performance of a motor. Increasing self-inductance and decreasing mutual inductance is an important method to improve fault tolerance of a FTPM-RDM. In this paper, the influence of positive wounded winding structure and positive and negative wounded winding structure of a 24-slot motor on the self-inductance and mutual inductance is analyzed by the winding function method. The conclusion that the positive and negative wounded winding structure has high self-inductance and low mutual inductance is revealed. This conclusion is generalized to the winding design in a general structure motor, and a generalized method of winding reconstruction is proposed. The simulation results show that the proposed winding reconstruction method can effectively increase self-inductance and significantly reduce mutual inductance to improve the fault tolerance of a FTPM-RDM. A motor prototype is produced and the experimental results have verified the correctness and feasibility of the proposed method.
Deep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances

Induction heating has become the reference technology for domestic heating applications due to its benefits in terms of performance, efficiency and safety, among others. In this context, recent design trends aim at providing highly flexible cooking surfaces composed of multi-coil structures. As in many other wireless power transfer systems, one of the main challenges to face is the proper detection of the magnetic coupling with the induction heating load in order to provide improved thermal performance and safe power electronic converter operation. This is specially challenging due to the high variability in the materials used in cookware as well as the random pot placement in flexible induction heating appliances. This paper proposes the use of deep learning techniques in order to provide accurate area overlap estimation regardless of the used pot and its position. An experimental test-bench composed of a complete power converter, multi-coil system and real-time measurement system has been implemented and used in this study to characterize the parameter variation with overlapped area. Convolutional neural networks are then proposed as an effective method to estimate the covered area, and several implementations are studied and compared according to their computational cost and accuracy. As a conclusion, the presented deep learning-based technique is proposed as an effective tool to estimate the magnetic coupling between the coil and the induction heating load in advanced induction heating appliances.
Fast Rough Bounds for the Coefficients of the Network Reliability Polynomial

The problem of fast estimating of reliability polynomial’ coefficients is considered in the paper in the case of all-terminal reliability (probability of a graph being connected). Obtaining lower and upper bounds for each coefficient of a reliability polynomial in one of its possible representation is based on counting all trivially known connected and disconnected realizations (states) of a random graph, based on easily derived information about graph’s structure, such as existence of chains and their lengths.
Blind Frame Syncword Detection Using Deep Neural Networks with Input Linear Filtering

In this paper, the problem of detecting a frame syncword (SW), which is repeated in received signal stream, is addressed. We propose a preprocessing technique for deep neural networks used for estimating the SW. As SW has a specific sequence, consecutive received signals are mapped to a symbol using a linear filter in order to make the neural network train their sequence. The performance of SW detection of multilayer perceptron (MLP) and convolutional neural network (CNN) depending on the frame length and the data preprocessing are evaluated. Simulation results show that CNN has better performance than MLP and data processing improves the performance of CNN.
On the use of ECG and EMG Signals for Question Difficulty Level Prediction in the Context of Intelligent Tutoring Systems

A fundamental drawback of traditional Intelligent Tutoring Systems (ITS) is that, unlike human tutors, they are not able to understand the emotional state of their users and adapt the learning process accordingly. This work explores the potential use of affective computing techniques for providing an affect detection mechanism for ITS. Electrocardiography (ECG) and electromyography (EMG) signals were recorded from 45 individuals that undertook a computerised English language test and provided feedback on the difficulty of the test's questions. Features extracted from the ECG and EMG signals were then used in order to train machine learning models for the task of predicting the self-perceived difficulty level of the questions. The conducted supervised classification experiments provided promising results for the suitability of this approach for enhancing ITS with information relating to the affective state of the learners, reaching an average classification F1-score of 75.49% for the personalised single-participant models and a classification F1-score of 64.10% for the global models.
AI-Enabled Wireless KPI Monitoring and DiagnosisSystem for 5G Cellular Networks

This paper introduces a novel AI framework for nationwide wireless KPI monitoring and its diagnosis in LTE/5G mobile networks. The proposed method, which has not been provided so far for a commercial use, automatically detects meaningful degradations of cell-specific wireless KPI statistics and also provides an alarm to administrator with possible causes of the corresponding problem. Machine learning and deep learning technologies are properly applied to detect relative KPI degradations on the basis of those estimated globa/local PDFs and also to carry out root cause analysis for the problem diagnosis, respectively. We have confirmed the feasibility of the mentioned AI framework by completing a first PoC through interworking our AI server with commercial LTE/5G networks. Currently, about 20 major monitoring KPIs such as RRE, ENDC,RSSI, BLER, and so on are being considered in current 5G NSA mode.
A Permutation Entropy-based Importance Measure for Condition Monitoring Data Fusion in Fault Diagnosis

In condition monitoring and fault diagnosis, how to measure the importance degree of different condition monitoring (CM) data before data fusion is a vital issue. We propose an importance measure that can be modeled using a weighted average function. The weight is measured with the relative scale of the permutation entropy from each fault feature variable. Compared with some other importance measures in data fusion, the proposed measure focuses on the degradation trend represented by the permutation entropy, instead of the information volume represented by the Shannon entropy. Then, a multiple fault feature variable fusion method based on the proposed importance measure is further proposed in the D-S evidence theory framework. Finally, a case study involving an oil analysis-based dataset from a power-shift steering transmission is carried out to investigate the superiority of the proposed method.
High Performance SDR for Monitoring System for GNSS Jamming Localization

This paper presents a basic description of a monitoring system which is designed for the GNSS jamming detection and localization as well as giving the theoretical issue together with specific consequences. The usage of that system is targeted to an aviation safety and space together with general transportation. The system is an extensive project, and this document is mainly about measuring station as an essential part of it. The paper presents specific localization techniques which will be used in digital signal processing. Several tests of the functionality of the measuring station have been made and presented in this paper. The result of the paper comes from the basic test of the measuring station. In that test, functionality - clock synchronization, mutual phase of an antenna array, software-defined radio was proved. The monitoring system is still developing and other important results will be described in the future.
Effectiveness of Motor Imagery combined to Action Observation in controlling Stroke-related Genu Recurvatum: A Randomized Controlled Trial

Genu recurvatum is one of the physical impairments that can affect functional recovery and independence after stroke. The aim of our study is to evaluate the effect of Motor Imagery (MI) combined to action Observation (AO) on the reduction of Genu Recurvatum in post-stroke patients. Twenty subjects were recruited and were randomly and equally allocated to two different groups. Experimental group received AO+MI protocol and the control group received a conventional rehabilitation care including stretching and functional rehabilitation. Outcome measures were static range of motion (during single leg stance on the affected limb) measured by an electrogoniometer and Gait recovery measured on the gait subcomponent of the Motor Assessment Scale (MAS). Subjects in the experimental group showed greater reduction in knee hyperextension. This was particularly evident when comparing static knee range of motion before and after each session. Additionally, reduction in knee hyperextension was found to be highly correlated to the improvement of gait recovery score of the MAS (r=0.86). These results suggest that a motor imagery protocol can be effective for the reduction of genu recurvatum in post-stroke patients.
Cooperative Brachytherapy for Prostate Cancer Under MRI Guidance

Prostate Cancer is one of the most prevalent cancers in men and a leading cause of death. Prostate Brachytherapy (BT) is one of the promising treatment techniques. The use of BT techniques is made easier, thanks to the developments of associated imaging technologies like Ultrasound and Magnetic Resonance Imaging (MRI). MRI has the capacity to detect small lesions providing a wider scope of research and enabling better accuracy in BT treatments. In order to enhance the diagnosis of the prostate cancer and its treatment options in the 2 seas region, Europe, Interreg 2 Seas co-financed a European Union Project Cooperative Brachytherapy called CoBra. The aim of the CoBra project is to perform a robotized biopsy and BT under real-time MRI-guidance. The concept is under development such as the robot’s task should be achieved within the MRI-scanner without any repositioning of the patient.
Privacy Mining of Large-scale Mobile Usage Data

While enjoying the convenience brought by mobile phones, users have been exposed to high risk of private information leakage. It is known that many applications on mobile devices read private data and send them to remote servers. However how, when and in what scale the private data are leaked are not investigated systematically in the real-world scenario. In this paper, a framework is proposed to analyze the usage data from mobile devices and the traffic data from the mobile network and make a comprehensive privacy leakage detection and privacy inference mining on a large scale of realworld mobile data. Firstly, this paper sets up a training dataset and trains a privacy detection model on mobile traffic data. Then classical machine learning tools are used to discover private usage patterns. Based on our experiments and data analysis, it is found that i) a large number of private information is transmitted in plaintext, and even passwords are transmitted in plaintext by some applications, ii) more privacy types are leaked in Android than iOS, while GPS location is the most leaked privacy in both Android and iOS system, iii) the usage pattern is related to mobile device price. Through our experiments and analysis, it can be concluded that mobile privacy leakage is pervasive and serious.
Enhanced Spatial Modulation aided OFDM System

Orthogonal frequency division multiplexing with Index Modulation (OFDM-IM) technique has recently attracted numerous research interests by applying the core idea of spatial modulation (SM) technology to the OFDM technology. In this paper, in order to further achieve the spatial and transmit diversity gain, a new scheme of enhanced spatial modulation (ESM) aided OFDM (ESM-OFDM) is proposed. This paper can be summarized as firstly, the ESM scheme is briefly reviewed. Then the technique of ESM-OFDM is elaborately introduced, which can carry more extra information bit compared to the OFDM-IM scheme. Furthermore, with the aid of maximum-likelihood (ML) detector, the asymptotic average bit error probability (ABEP) is presented. Finally, compared to conventional schemes such as OFDM, OFDM-IM, simulation results demonstrate that the spatial and transmit diversity gain is achieved to enhance the reliability over Rayleigh distribution and additive white Gaussian noise (AWGN) in wireless communication network.
Tiny Image Classification using Four-Block Convolutional Neural Network

The task of classifying images into predefined classes is a major problem in computer vision and artificial intelligence. Deep neural network such as Convolutional Neural Network (CNN) have shown great success in large-scale dataset of high resolution image classification. Here, it is important to note that most real time images may not have high resolution. With the increasing demand of surveillance camera-based applications, the low resolution images are major problem. To overcome this problem, we propose two Four-Block CNN model; one with four-layers and the other one with three-layers. Our proposed Four-block four-layer CNN model contains four convolution layers, first three layers contains 3 × 3 kernel size with stride-1 and fourth layer used with stride-2 for dimensionality reduction.The Four-block three-layer has two convolutional layers with stride-1 and third layer with stride-2. We trained our model on CINIC-10 and CIFAR-10 datasets having low-resolution images. For taking average of whole feature map we are using Global Average Pooling layer as a classifier in both models. To reduce training time complexity, we use non-saturating neurons. Overfitting problem has been addressed by dropout and batch normalization methods. On the validation data, we achieved the best accuracy of 81.62%, 92.21% for CINIC-10 and CIFAR-10 respectively, using Four–block four–layer model.
Node Influence Calculation of Novel Social Networks

Identifying Influential nodes in complex networks is of great significance for network structure optimization and robustness enhancement. To measure the role of nodes, many classic methods could help identify the influential nodes. In this paper, we take the simple complex network composed of Jin Yong’s novels as an example, applying the classic measurement methods for key nodes to analyze them. In the introduction section, we will briefly introduce background knowledge of complex networks, and measurement methods for key nodes in complex networks. In experiments, we will analyze each measurement method in detail for influential node as well as sum up the advantage and disadvantage of each method.
Recent advances in Glioma grade classification using machine and deep learning on MR data

Glioma grade classification based on Magnetic Resonance (MR) data and using Machine Learning approaches is a hot topic. Recently, considerable improvements have been made in this field especially in the last two years. This paper reviews a selection of the most recent methods from 2018 and 2019, details their preprocessing and priors, such as the different modalities used in their datasets. It then groups the different approaches by comparing their different learning scheme. While classical machine learning is present, more and more authors are using Convolutional Neural Networks. Multimodal MR sequencing, such as perfusion imaging, diffusion imaging or MR spectroscopy, gives an interesting diversity of information. Works using these modalities often reach an interesting accuracy level in classification.
Flexible Refocusing Method for Camera Arrays Based on Reprojection

Light field provides enough information so that one can post-process images after acquisition. A representative application of light-field is refocusing, which changes the in-focus region of images. In this paper, a flexible algorithm of refocusing is proposed for images captured by camera arrays. By utilizing the parameters estimated by camera calibration, the reprojection can be used to produce images refocused on arbitrary plane. Specifically, this method allows one to change the focal plane flexibly by simply altering the function of desired refocusing plane. The efficiency of the proposed algorithm is demonstrated by experiments using real-scene data obtained by the self-developed light field gantry.
Analysis and Mitigation of Harmonics for Standard IEEE 13 Bus Test System Using ETAP

The main intension of any electrical utility company is to deliver power with best quality. By expanding the utilization of power electronic gadgets (nonlinear loads), sinusoidal waveforms of current and voltage gets deformed and deviated which leads to deterioration in power system quality. One of the important causes of improper power quality is power system harmonics and harmonic analysis is needed to investigate in modeling components to minimize or remove this harmonic current or voltage. This paper presents load flow, short circuit and harmonic analysis which is executed applying adaptive newton raphson method, impedance method and harmonic load flow method resp. on IEEE 13 bus system with linear and non-linear loads. Also, the solution is recommended towards the end for the maintenance of harmonics within permissible limits using ETAP software.
Regressions of Clustered Gene Expression Data Manifest Tumor-Specific Genes in Urinary Bladder Cancer

Bladder cancer or urinary bladder cancer, is a common neoplasm of the urinary tract, with higher prevalence in men aged 60 to 70 years. In the present work we have used gene expression microarray data both from in-house experimentation, as well as from publicly available microarray data. We have used bioinformatics analyses as well as regression methodologies, in order to find common gene expression profiles with respect to tumor subtypes and differentiation. Our approach included gene clustering with k-means, and gene functional annotation. We have found several gene groups that manifest common expression profiles and also we have identified clusters of genes that manifested an ascending or descending pattern with respect to tumor differentiation and subtype. Such approaches could prove useful to the identification of noel gene targets that could be utilized as prognostic, diagnostic and therapeutic targets.
A Fault Detection and Isolation Scheme for Dual-channel Speed Sensors

High-speed train dual-channel speed sensors are often influenced by factors like dusts, vibration, temperature changes, which easily cause false or miss alarm, even wrong isolation. The experimental comparison method commonly used in practical engineering, this method cannot detect faults in time or accurately isolate multiple faults. In this paper, a novel fault detection and isolation (FDI) scheme for dual-channel speed sensors is proposed by using the improved principal component analysis (PCA) and the improved reconstruction-based contribution plots (IRBCP). Take the dual-channel speed sensor of high-speed train as an example, experimental results show that the proposed scheme can satisfy FDI requirements of speed sensors, and is accurate and effective.