Miracle-dz/UniFAR-FTUs huggingface dataset:
A Real-Time IoT Based Arrhythmia Classifier Using Convolutional Neural Networks

With one in every four deaths that occur every year being due to a heart-related ailment, it is of utmost importance to study the symptoms, features, and cures for heart diseases so that timely action can be taken to detect and prevent fatalities. Arrhythmia is a type of heart ailment where the heart rate is irregular. It is caused due to erratic behavior of the electrical impulses that control the heartbeat. Detection and classification of arrhythmia are conventionally done manually by expert cardiologists through meticulous analysis of the electrocardiogram (ECG) waveform. Automatic and real-time ECG detection and analysis has gained importance in recent years especially due to the accelerating pace of advances in medical technologies. Therefore, the proposed system takes in data from an ECG sensor module (AD8232) and provides it as input to a trained Convolutional Neural Network (CNN) model in real-time. This model is capable of detecting various types of arrhythmias with accuracy greater than 90%. The classification results are then presented to the user through an interactive mobile application. A caretaker is also a part of the system, who is notified if in case the user's condition turns critical. Although a number of arrhythmia classification systems are implemented, the ease of access by the user and the interactiveness is highly limited. The implementation presented in this paper aims at providing an engaging user experience without compromising the performance and accuracy of measurements and predictions.
Modular Structure of the Intelligent Oil Production Equipment Control Station

A promising approach to the construction of an intelligent control station for a bore-hole sucker rod pump unit is presented. The approach is based on the decomposition of each specific control station of various manufacturers into permanent and variable parts in order to further expand the functionality by adding new modules: monitoring and diagnosing the state of ground and underground equipment, controlling operating modes based on the flow rate estimation and the module for determining the equilibrium part of the aerial part rocking machine. As an example, in addition to the previously implemented modules, the task of global optimization based on evolutionary calculations is posed and solved, which allows one to choose the balancing parameters of the aerial part of the rocking machine. The proposed modular principle of building an open extensible architecture of an intelligent control station provides both rational design and simplification of field operation.
Influence of a slotted flap gap size on the aerodynamic characteristics of a light aircraft wing at taking off and landing

The purpose of the study is to examine the impact of the gap size of a slotted flap on the aerodynamic characteristics of a light aircraft wing at take-off and landing. All surveys in the paper are numerical, executed in Ansys Fluent, using transient analysis and dynamic mesh method. The models used are 2 dimensional. Statements are made for the aerodynamic efficiency of a wing with a slotted flap, depending on the slot size. Optimal gap size is defined, for a specific wing configuration. Comparisons between the different slotted flap configurations and a plain flap wing design are made. The main goal of this study is the evaluation of the gap size of a slotted flap wing design upon the aerodynamic efficiency and determination of an optimal gap size for the examined wing.
Finding Meaningful Detections: False Discovery Rate Control in Correlated Detection Maps

The detection of faint sources is a key step in several areas of signal and image processing. The reliability of the detection depends on two key components: (i) the detection criterion used to derive detection maps in which the signature of a source takes the form of a detection peak, and (ii) the extraction procedure identifying the meaningful detections.In this work, the expected false discovery rate guides the selection of meaningful detections. A procedure is designed to account for correlations in the detection maps. This prevents the issue of the multiple detections of a single source and corrects the number of effective independent tests performed. The proposed approach is evaluated on an astrophysical application: the detection of exoplanets by high-contrast imaging.
The Improvement of Remaining Useful Life Prediction for Aero-engines by Injecting Noise

The remaining useful life (RUL) prediction methods for rotating machines have been extensively studied in recent decades. In most of the existing RUL prediction methods, noise is always regarded as the disgusting disturbance and tried to remove from the observation data. In contrast, noise can be beneficial if properly used. This paper proposes a new RUL prediction method which is boosted by the injection of intentionally-added noise into a long short-term memory (LSTM) neural network. The intentionally-added noise satisfies the noisy-expectation maximization (EM) positivity condition, and boosts the RUL prediction results. Therefore, the proposed method is referred as a noise-boosted RUL prediction method. This paper is a preliminary attempt when predicting RUL for aero-engines by using intentionally-added noise. The popular C-MAPSS lifetime dataset for aero-engines is used to validate the effectiveness of the new method. The prediction results show the improvement of prediction accuracy by the injection of intentionally-added noise.
Shape-Based Glioma Mutation Prediction Using Magnetic Resonance Imaging

Gliomas are the most frequently occurring primary brain tumors. Determination of the IDH-mutation (Isocitrate De-Hydrogenase) in these tumors improves classification and predicts survival. Currently, the only way of determining the mutation status is through a brain biopsy, which is an invasive procedure. This paper concerns the classification of a brain tumor’s mutation status through medical imaging. This study proposes a method based on shape description and machine learning. Magnetic resonance images of brain tumors were manually segmented through contour drawing, then analyzed through mathematical shape description. The extracted features were classified using multiple algorithms of which Random Undersampling Boosted Trees gave the highest accuracy. An accuracy of 86.4% was found using leave-one-out cross-validation on a data set of 13 IDH-positive and 9 IDH-wild-type gliomas. The results indicate the feasibility of the proposed approach, but further research on a larger data set is required.
CSP-based discriminative capacity index from EEG supporting ADHD diagnosis

The Attention-Deficit/Hyperactivity Disorder (ADHD) is a childhood-onset neurological disorder that can persist in adolescence and adult life, reducing concentration, memory, and productivity. Although biomarkers as TBR and P300 rely on ADHD physiology, differences between ADHD and control lack significance. In this work, we propose a feature extraction approach based on the common spatial patterns (CSP) from EEG signals to support the ADHD diagnosis. Our features quantify the channel-wise discriminative capacity from the resulting spatial patterns and eigenvalues. We validated the proposed methodology using synthetic and real EEG signal. In the former, the proposed index suitably identifies the spatial location of differentiating sources, while attenuates the common activity. In the latter, the resulting subject-wise features fed a linear discriminant analysis as the supported-diagnosis tool. Achieved 87% accuracy rate proves that the discriminative index identifies outperforms conventional biomarkers in the ADHD diagnosis.
Full-Duplex mmWave Communication with Hybrid Precoding and Combining

We investigate the design of hybrid precoders and combiners for a millimeter wave (mmWave) point-to-point bidirectional link in which both nodes transmit and receive simultaneously and on the same carrier frequency. In such full-duplex configuration, mitigation of self-interference (SI) becomes critical. Large antenna arrays provide an opportunity for spatial SI suppression in mmWave. We assume a phase-shifter based, fully connected architecture for the analog part of the precoder and combiner. The proposed design, which aims at cancelling SI in the analog domain to avoid frontend saturation, significantly improves on the performance of previous approaches.
Modeling and Analysis of High-Frequency MMC Impedance Considering Different Control Modes and Voltage Feedforward

The sequence impedance of modular multilevel converters (MMC) is the premise to analyze the stability of MMC-based high voltage direct current (HVDC) system. However, few papers have modeled MMC impedance with comprehensive consideration of different control modes and voltage feedforward, the high-frequency impedance characteristics of the MMC have not been intensively studied, either. In fact, the voltage feedforward has an important influence on the high-frequency impedance characteristics of the MMC, which can not be ignored. This article proposes a sequence impedance for MMC considering complete control loops. Not only current inner-loop, circulating current control loop and phase-locked loop (PLL) are considered, but also different control modes including power control, DC voltage control and AC voltage control are considered. It is worth mentioning that especially the voltage feedforward and time delay are taken into account. Based on this impedance model, the characteristics of the MMC are thoroughly analyzed, and the analysis shows that the voltage feedforward and time delay will greatly affect the high-frequency characteristics, which will deeply affect the stability of the system. Meanwhile, the influences of PLL dynamics, outer-loop control, circulating current control and parameters of controller on the sequence impedance are also analyzed in detail.
A Lightweight SOA-Based Network Slicing Creation System

The next-generation network system is envisioned to be a multi-service network supporting different applications with multiple requirements. In this vision, Network SLicing (NSL) is considered a key mechanism to create multiple virtual networks over the same physical infrastructure. However, it is a challenging problem to deploy NSL with great flexibility and full automation. In this paper, we propose a novel lightweight SOA-based NSL creation system, which includes the design domain, execution domain, and infrastructure domain. The design domain provides an easily-operating service design environment. Besides, we define a detailed description of NSL named Network SLicing Descriptor (NSLD), illustrating the resource and requirements for designing network services. The execution domain is a service execution environment with multi-tenancy support. This domain based on micro service architecture is distributed and self-organized. The infrastructure domain contains multiple network infrastructure resource domains that enable micro services. With these components, tenants can create NSL freely via an intuitive and easy-to-use UI on a web browser. We also implement an IoT NSL scenario to solve the multi-flow transmission problem. After adding sub-services and QoS policy into the design domain, the scenario can run automatically at the execution domain.
The Impact of Channel Correlation on the System Performance and Quality of Service in 5G Networks

In the next-generation 5G networks, Heterogeneous Networks are widely used to achieve greater spatial diversity. On the other hand, tens of thousands of devices are expected to be connected to the Internet and and their dense concentration in a given area will have significant consequences on the benefits of spatial diversity, which will affect the overall performance. This paper examines the degree of influence that channel correlation may have on the performance of the protocols cooperative MAC protocols, applicable in 5G networks. Some effects of the physical layer, and in particular, the correlated shadowing impact on the system performance and QoS is evaluated using Monte Carlo simulations.
Functioning and Development of a Real-Time Information System for the Oil Treatment Technological Process Control

The paper describes the difficulties of formalizing the processes of production, transportation and preparation of oil production of wells, particularly in the construction of a mathematical model. The process of creation of a SCADA system for supervisory control and data acquisition system is considered. To manage a remote object, transmitted parameter values to the upper level of automation and incoming influences analysis is presented, as well as timely process control at the booster pump station, an OPC server and client have been developed. The importance of the presence of a specialist for managing the facility both in normal operating modes and for making decisions in the event of non-standard modes is noted. A graphical user subsystem has been developed for system parameters monitor, control and regulation.
Robotic Arm Pick-and-place System for L-Shaped Water Pipe Joint with Arbitrary Angle

The system combines a camera and a mechanical arm to complete the L-shaped water pipe joint picking system with arbitrary angle. The system is mainly divided into two parts: computer side and arm side. The computer is responsible for observing the current position and angle of the workpiece, receiving the image of the workpiece and performing image processing operations (such as binarization, contour detection), and at the same time converting the coordinate and angle of the workpiece into the coordinate and angle of the robot arm and transmitting it End. The arm end receives the coordinates and angle from the computer end and performs the corresponding picking action. The system sets the workpiece center in combination with the rotation point and picking point in the minimum contour rectangle where the workpiece is found to binarization to pick the workpiece. During the experiment, the conditions of the factory environment were also simulated. During the day, noon and night, four, three, two and one fluorescent lamp were turned on, and 100 tests were performed on 12 situations. The average success rate is as high as 99%. Experiments show that the system can be actually used in factories, saving related manpower and material resources.
Optimized 3D Scene Rendering on Projection-Based 3D Displays

We address the problem of 3D scene rendering on projection-based light field displays and optimizing the input display images to obtain the best possible visual output. We discuss a display model comprising a set of projectors, an anisotropic diffuser and a viewing manifold. Based on this model, we render an initial set of projector images to be further optimized for the best perception at a specified set of viewing positions. We propose a least squares method, which minimizes the channel-wise color difference between the generated images for different viewer positions, and their ground-true counterparts. We formulate a constrained optimization problem and solve it iteratively by the descent method.
Joint Calibration and Tomography based on Separable Least Squares Approach with Constraints on Linear and Non-Linear Parameters

Most of the existing tomography techniques rely on accurate calibration to reconstruct the features of interest. In several industrial applications, the calibration is typically performed off-line and has to be repeated frequently to counter time varying perturbation caused by aging, operating conditions, and so on. In this paper, a novel online joint calibration and tomography method based on variable projection based separable least squares approach with constraints on linear and non-linear parameters is proposed. The constraints on the linear parameters improve the estimation accuracy of the ill-posed and under determined tomography problem. The constraints on the non-linear parameters restricts the proposed method from departing far away from the initial guess, especially when a good initial guess is available. The proposed method is used to reconstruct the temperature distribution inside a blast furnace and simultaneously to calibrate the positions of acoustic transducers based on simulated acoustic time of flight measurements.
Deep Unsupervised Workload Sequence Anomaly Detection with Fusion of Spatial and Temporal Features in the Cloud

The abnormal detection of the workload sequence is designed to achieve intelligent operation and management of the cloud platform to improve operational efficiency. Due to the diversity of workload sequence variation patterns in the large-scale cloud, it is difficult for the traditional abnormal detection methods to extract features effectively, which leads to detecting anomalies inaccurately. In this paper, a deep unsupervised anomaly sequence detection model with fusion of spatial and temporal features of workload sequence (TS-DeepSVDD) is proposed. To extract the spatial and temporal features of the workload sequence, the model introduces convolutional recurrent neural network (CRNN) to improve network architecture with deep support vector data description (DeepSVDD). First, a convolutional neural network (CNN) module extracts the spatial features of the workload sequence. Second, for the acquired spatial features vectors, the bidirectional long short-term memory (BiLSTM) module extracts temporal features. Finally, the features vectors training support vector data description (SVDD) classifier fuses with the deep features of the workload sequence spatial and temporal features. TS-DeepSVDD extracts the deep features of the workload sequence from the spatial and temporal dimensions. It achieves a comprehensive description of the inherent law of the workload sequence, increasing the differentiation between normal and abnormal sequences. The simulation dataset and Google trace dataset are used respectively for verification. The results show TS-DeepSVDD can detect different abnormal sequences more accurately than the traditional unsupervised anomaly detection methods.
Design of a Non-negative Neural Network to Improve on NMF

For prediction of a non-negative target signal using a non-negative input, we design a feed-forward neural network to achieve a better performance than a non-negative matrix factorization (NMF) algorithm. We provide a mathematical relation between the neural network and NMF. The architecture of the neural network is built on a property of rectified-linear-unit (ReLU) activation function and a convex optimization layer-wise training approach. For an illustrative example, we choose a speech enhancement application where a clean speech spectrum is estimated from a noisy spectrum.
Ventricular response regularity in atrial fibrillation and its relationship to successful catheter ablation

Atrial rate is known to modulate ventricular response during atrial fibrillation (AF). The resulting pulse irregularity translates into widely varying inter-beat intervals (IBIs) extracted from recorded surface electrocardiogram (ECG) activity. In AF, the random nature of the IBIs makes them difficult to analyze using traditional methods, and little work has investigated the relationship between ventricular response and persistent AF (persAF) disease progression. In this paper, we propose nonlinear approaches for characterizing IBI dynamics in patients undergoing catheter ablation for the treatment of persAF.
Incorporating User Feedback Into One-Class Support Vector Machines for Anomaly Detection

Machine learning and data-driven algorithms have gained a growth of interest during the past decades due to the computation capability of the computers which has increased and the quantity of data available in various domains. One possible application of machine learning is to perform unsupervised anomaly detection. Indeed, among all available data, the anomalies are supposed to be very sparse and the expert might not have the time to label all the data as nominal or not. Many solutions exist to this unsupervised problem, but are known to provide many false alarms, because some scarce nominal modes might not be included in the training dataset and thus will be detected as anomalies. To tackle this issue, we propose to present an existing iterative algorithm, which presents potential anomaly to the expert at each iteration, and compute a new boundary according to this feedback using One Class Support Vector Machine.
Deep-learning-based tracking-error prediction for two-axis machining

Developments in deep learning have led to artificial neural networks being widely used in various fields. This paper develops a deep-learning-based tracking-error predictor for two-axis machining. Randomly generated G-code commands are used for training and testing. The implementation strategies are evaluated to find a suitable configuration with improved prediction accuracy. The inference performance of different end-devices is also evaluated. For the tested trajectories, the experimental results show that the root mean square errors of the predicted tracking errors are 0.143 mm for the x-axis and 0.064 mm for the y-axis.
EM Fingerprints: Towards Identifying Unauthorized Hardware Substitutions in the Supply Chain Jungle

This paper proposes a system capable of branding digital device components based on the EM signals typically emitted during their normal operational cycles. Such signals contain digital artifacts that are unique, which may act as an identifier of a particular device component e.g., its CPU, or the entire device if one chooses to take into account a combination of multiple such components. In real-life scenarios, this “bio-metrical” fingerprinting of hardware has to be conducted only once, possibly as part of an initial device configuration process with minimum additional maintenance time and cost, by the network administrators. At a subsequent stage, devices can get “authenticated” by comparing their newly emitted signals against the preexisting database during routine checks. The experimental results attest that the proposed approach can effectively protect a network against unrecognized potentially rogue devices posing as benign or malicious substitutions of hardware components at the chip level with near-perfect accuracy. One may view the proposed system as a technical solution to verify the trustworthiness of digital parts as well as the actors involved in certain stages of the supply chain.
A machine learning approach for image retrieval tasks

Several methods based on visual methods (BoVW, VLAD,…) or recent deep leaning methods try to solve the CBIR problem. Bag of visual words (BoVW) is one of most module used for both classification and image recognition. But, even with the high performance of BoVW, the problem of retrieving the image by content is still a challenge in computer vision. In this paper, we propose an improvement on a bag of visual words by increasing the accuracy of the retrieved candidates. In addition, we reduce the signature construction time by exploiting the powerful of the approximate nearest neighbor algorithms (ANNs). Experimental results will be applied to widely data sets (UKB, Wang, Corel 10K) and with different descriptors (CMI, SURF).