Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Evaluation on NB-IoT and LoRaWAN for Industrial and IoT Applications

Low power and long-range communications are essential features of the Internet of Things (IoT) paradigm that is becoming widespread across a spectrum of industrial applications. In this paper, we present performance evaluation of the most promising long-range communication technologies, namely LoRaWAN and NB-IoT. We present accurate in-field measurements using a monitoring application as a testbench for a fair comparison in terms of energy efficiency and lifetime. Experimental results highlight that NB-IoT payload length does not impact on transmission energy. Thus, applications that implement buffering and caching techniques are favored. On the other hand, LoRaWAN consumes 10× less energy to transmit a payload equivalent to that of NB-IoT, thereby allowing longer end-device lifetime.
Predictive use cases of CNN based multi label classification for programming languages

Multi-label classification refers to classifying data into two or more, usually independent, set of output labels. This approach is suitable for deep learning applications in multi-faceted subjects like software development, where it is desirable to yield multiple outcomes. This paper proposes a CNN based deep learning model on public datasets of programming language platforms like GitHub and Stack Overflow to infer intelligence to aid decision making process regarding the choice of programming languages for a given software development requirement. For this research, we’ve developed a training model with pre-trained vector embedding layer and multi-channel one dimensional CNN layers, followed by Multi Layer Perceptron layer to provide multi label outputs. We have managed to achieve 92%, 98% accuracy and 22%, 4% loss with our two experimental setups for Github and Stack Overflow respectively. The model performed well when tested on software development requirements. Stack Overflow dataset was observed to be noticeably better performing than the Github dataset for actual software development use cases. The implications of these models were also found to be good for trend prediction and source code use cases.
Vehicular Delay Tolerant Network Routing Algorithm Based on Bayesian Network

Delay Tolerant Networks (DTNs) are novel wireless mobile networks, which suffer from frequent disruption, high latency, and the lack of a complete path from source to destination. Vehicular Delay Tolerant Network (VDTN) is a special type of DTNs with vehicles as nodes. In VDTN, most nodes have specific movement patterns, however, traditional routing algorithms in DTNs do not take this characteristic into considerations very well. In this paper, a new routing algorithm based on Bayesian Network (BN) is proposed to construct the prediction model, which intends to predict the movement patterns of nodes in the real VDTN scenarios. Firstly, a comprehensive BN model is established, where more attributes of nodes are selected to improve the accuracy of the model prediction. Then, considering the complexity of the structure learning problem of BN, a novel structure learning algorithm, K2 algorithm based on Genetic Algorithm (K2-GA), is proposed to search the optimal BN structure efficiently. At last, Junction Tree Algorithm (JTA) is adopted in the inference of BN, which can accelerate the inference process through variable elimination and calculation sharing for large scale BN. The simulation results show that the proposed VDTN routing algorithm based on the BN model can improve the delivery ratio with a minor forwarding overhead.
Fall Detection with Supervised Machine Learning using Wearable Sensors

Unintentional falls can cause severe injuries to a person, and even death, especially when no immediate assistance is provided. The aim of Fall Detection Systems (FDSs) is to detect the occurrence of a fall and to automatically and promptly request the necessary assistance. This work proposes a FDS based on wearable sensors - i.e., accelerometers and gyroscopes – and Machine Learning (ML), for sensor signal processing and detection. The process extracts a number of features on portions of the signal and classifies them as falls or regular daily activities. The classifier is a Support Vector Machine (SVM) that is trained using a manually labelled dataset, where human activities are distinguished between falls and regular activities. The method is assessed on the publicly available SisFall dataset, with extended annotation, and compared with the results obtained in the literature for the same dataset; the proposed method largely outperforms the original analysis technique proposed for the SisFall dataset, with an F1 score higher than 97% and a recall higher than 99.7%.
Quadrotor Robust Optimal Attitude Tracking Control subjected to Model Uncertainties and External Disturbances

Quadcopter unmanned aerial vehicles continue to play important roles in several applications and the improvement of their control performance has been explored by a great number of studies. In this paper, we present a robust optimal H∞ attitude control algorithm for a quadcopter tracking either a step or a moving attitude reference. We model the system model uncertainties by using the linear fractional transformation approach and take it into account in combination with external disturbances during designing the controller. To demonstrate the effectiveness of the proposed method, numerical simulations were conducted and our controller performance is compared to a conventional PID controller.
Network Intrusion Detection Based on Xgboost Model Improved by Quantum-behaved Particle Swarm Optimization

This paper proposes the method of optimizing the Xgboost model by Quantum-behaved Particle Swarm Optimization (QPSO), and validates its effectiveness in the context of network intrusion detection. We preprocess the data set first, and then use QPSO to optimize the parameters of the Xgboost model. After that, we use the test set to evaluate the optimized model. The experimental results show that the Xgboost model optimized by QPSO has good performance in terms of precision, recall rate and average precision, and is better than the unoptimized Xgboost model and grid search method.
Improved Topology of Symmetrical Multilevel Inverter With Reduced Number of Switching Devices

In this paper, an improved topology of symmetrically operated multilevel inverter with reduced number of switches is proposed. A factor of merit about this topology is that it allows cascaded configuration to produce a higher number of output levels. Additionally, an appropriate technique for the required pulse width modulations (PWMs) on the switches is also described. The topology is compared with several classic and newer reduced switch multilevel inverters (MLI) in terms of the number of switches, DC sources, output levels and basic units. An in-depth study on the 9-level (9-L) operation of the proposed topology is presented where the voltage, current and power-sharing by each DC source are analysed. The power shared by the first DC source is found to be the highest, followed by the second, third and fourth DC sources. From the ideal simulation using Matlab Simulink environment, the proposed topology is capable to deliver power to the load at almost unity power ratio of above 99%, rendering it very much suitable for real time application. Furthermore, the study is extended to include the cascaded 17-level (17-L) operation of the proposed topology. It is noted that higher cascaded configuration of the proposed topology will allow higher number of output levels, resulting in lower total harmonic distortion (THD).
Deep CNN-Based Ensemble CADx Model for Musculoskeletal Abnormality Detection from Radiographs

Musculoskeletal Disorders (MSDs) are excoriations and afflictions that assail body movement of human. In present days diagnosis of musculoskeletal conditions are dependent on radiographs. Sometimes doctors or radiologist can make an error that can mislead the diagnosis of abnormalities. So, we have been motivated to develop a novel Computer-Aided Diagnosis(CADx) system based on Deep Convolutional Neural Network (Deep CNN) that will help the doctors to identify musculoskeletal abnormalities through radiographs. We have used VGG-19, ResNet architecture to build a model for four types of study (Elbow, Wrist, Finger, and Humerus). 5-fold cross-validation method is also applied to evaluate our models. Then we have applied ensemble techniques to improve the model’s performance. Finally, based on some performance evaluating metrics the best one is selected for each of the study types and in aggregate. Our proposed technique tested on a benchmark radiographic dataset named ‘MURA’, and the final result is compared to other prominent techniques. For Elbow, Finger, Humerus, Wrist study, model performance was consecutively 86.45%, 82.13%, 87.15%, and 87.86%. Experimental consequences show that our proposed method is a condign strategy to resolve musculoskeletal abnormalities detection.
Novel Real-Time Energy Management in Hybrid Electric Vehicle

Electric vehicles are clean alternatives to traditional vehicles. Fuel cell and lead-acid batteries are the two sources considered for electric vehicle energy management in this paper. Designing of efficient control system algorithm for electric vehicles is the key to long lasting life of the sources. The primary role is to analyze how energy demand of drive cycle data is managed between different sources in electric vehicles. In this paper, simulation and hardware implementation of energy management algorithm is implemented by TI’s DSC28335. This novel method of power distribution is successfully implemented through simulations and hardware.
Virtual Sensor Development Based on Reduced Order Models of CFD Data

Manufacturing industry is facing the challenge to cope simultaneously with the high demanding trends of the market (i.e. personalization) and the high quality standards. In this context, it is of utmost importance the implementation of zero-defect strategies relying on sensing and control systems to improve automation and minimize failures. Non-invasive physical sensing is not always feasible. In those cases, virtual sensors may stand for a great tool to support the development of enhanced control systems. In this work, a new methodology for building virtual sensors is presented. The novelty of the methodology relies on the coupling of two modeling techniques: detailed computer-aided engineering (CAE) simulations and model order reduction (MOR) techniques. The result is a realtime simulation model that can provide the variables required by the control system, using other available data (physically sensed) as input. Hence, the coupling between virtual and physical sensors can provide a richer description of the process to improve the process control and performance. To demonstrate the methodology, a case study is presented, where the virtual sensor is aimed at defining the flow pattern (fluid interface position) within a flow-focusing microfluidic device. The resulting virtual sensor can predict in real-time the shape and location of a multiphase fluid interface inside the microchip, based on the volumetric flow rates measured at the chip inlets. The interface position monitoring provided by the virtual sensor is of great relevance to improve the control and performance of such a microfluidic system.
Computationally Enabled Minimum Total Energy Tracking for a Performance Regulated Sub-Threshold Microprocessor in 65-nm CMOS

This article describes an architecture that auto- nomously sets the operating supply voltage (<inline-formula> <tex-math notation="LaTeX">$V_{dd}$ </tex-math></inline-formula>) of a digital system for minimum total energy dissipation, inclusive of regulator loss. The technique directly computes the energy per cycle (EPC) delivered by the supply to the system, to guide <inline-formula> <tex-math notation="LaTeX">$V_{dd}$ </tex-math></inline-formula> adjustment for minimum energy-per-cycle point (MEP; voltage: <inline-formula> <tex-math notation="LaTeX">$V_{MEP}$ </tex-math></inline-formula> and frequency: <inline-formula> <tex-math notation="LaTeX">$f_{MEP}$ </tex-math></inline-formula>) tracking. The proposed approach avoids significant <inline-formula> <tex-math notation="LaTeX">$V_{dd}$ </tex-math></inline-formula> margins typically required for system operation near-threshold and at sub-threshold <inline-formula> <tex-math notation="LaTeX">$V_{dd}$ </tex-math></inline-formula> levels by employing a unified clock and power (<italic>UniCaP</italic>) architecture that regulates target performance across process, voltage, and temperature (PVT) variation with minimal guardband requirements. Measurement results from a 65-nm CMOS test chip demonstrate the <inline-formula> <tex-math notation="LaTeX">$V_{MEP}$ </tex-math></inline-formula> tracking with less than 5 mV of error within the operating voltage range of 0.38–0.58 V. Measurement results also demonstrate the MEP tracking with temperature variation from −30 °C to 105 °C across varied loading conditions over 20 different test chips.
Path Following Controller Design for an Underactuated Hovercraft with External Disturbances

This paper addresses the problem of path following control for an underactuated hovercraft in the presence of external disturbances and uncertain parameters. By using the backstepping technique, a path following controller is proposed which is able to steer the hovercraft to a neighborhood of a desired path, obtaining global practical stability. In order to make the vehicle robust to external disturbances and uncertain vehicle parameters, a group of disturbance and linear damping coefficients estimators are designed. Simulation and experimental results are presented and analyzed, validating the efficiency of the proposed control strategy.
Toward Tactile Internet

Leads with the recent progress and development in the field of robotics and haptic devices, Tactile Internet became the next communication wave after the internet of things (IoT). Far from the traditional Internet in which audio and visual senses can be transferred, Tactile Internet is a new communication paradigm that supports the haptic communications in real time and provides a new way for the human-to-machine (H2M) communication. There are many challenges associated with the Tactile Internet, however the 1ms end-to-end latency remains the most considerable.In this work, we provide a better understanding of the Tactile Internet system, by defining the main system parameters, components, design requirements and design challenges of the system. We provide the recent progress for realizing Tactile Internet and the main work introduced to provide appropriate solutions for the design issues. A vision of the Tactile Internet system realization and system applications is introduced. Moreover; we investigate the market reflection on Tactile Internet development and realization and a time plan based on the market progress and the academic research is set, to predict away for the Tactile Internet.
Closed Loop Operation of Induction Machine using MRAS based Sensorless Scheme

Sensorless control of electrical machines are now extensively used not only in motoring mode but also preferred in power generation due to its smaller size with lower maintenance and cost. This requires the sensorless control scheme for speed estimation to be investigated extensively in various aspects. Therefore, the paper presents the performance evaluation of closed loop model reference adaptive system (MRAS) based sensorless scheme for speed estimation in induction machine drive using field oriented control. Operation of induction machine drive has been investigated for parametric variation and current harmonic content in both generating and motoring mode in MATLAB/Simulink environment.
Reachable set estimation for discrete-time switched nonlinear positive systems with time delays

This paper studies the reachable set estimation for discrete-time switched nonlinear positive systems with time delays whose vector fields are homogeneous of degree one and order-preserving. By using a new analytical technique, we establish necessary and sufficient conditions such that all solutions of the system exponentially converge to a ball under average dwell time switching. Compared with the existing results, the average dwell time given in this paper is less conservative. Finally, numerical examples are given to illustrate the effectiveness of our results.
A Noise Suppression Method for Partial Discharge Based on Two-dimensional SVD

The analysis of PD measurements in the field is often hampered by electrical noise, which necessitates the application of sophisticated digital signal processing techniques. Aiming at the problem of noise suppression of partial discharge (PD) signals, this paper proposes a new de-noising method based on generalized S transformation and singular entropy incremental curvature spectrum. Firstly, the two-dimension time-frequency matrix of noise-contaminated PD signals is obtained by generalized S-transform, which is used as the trajectory matrix of singular value decomposition (SVD). then SVD is performed to calculate the singular entropy incremental curvature spectrum; A near-zero point is set to determine the threshold point to remove white noise; Finally, the denoised PD waveform is obtained by generalized S inverse transformation. The proposed method is applied to simulation signals and measured signals, and its results are compared with those of traditional wavelet denoising method and adaptive singular value decomposition (ASVD) denoising method. The results show that: compared with the other two methods, the denoising effect of the proposed method is better, and its results are more similar to the original PD signals.
A 2-20 GHz SiGe Amplitude Control Circuit with Differential Signal Selectivity for Wideband Reconfigurable Electronics

This paper presents a wideband integrated single-tap circuit for a general-purpose, high-performance transversal filter application, and which has been implemented in silicon germanium (SiGe) technology for use in direct-throughput processing of RF signals in the microwave and millimeter-wave spectrum. To provide wideband instantaneous reconfigurability and multi-function RF capability, the proposed circuit consists of a digital step attenuator, an active-based balun, a single-pole single-throw (SPDT) switch, and a digital control circuit. To achieve low noise characteristics for the receiver, a wideband low-noise amplifier (LNA) is included in the design. In addition, an active-based switchable power divider is utilized for supporting open-loop systems that provide a comparison with system set points or input signals. The proposed single-tap circuit demonstrates flat in-band gain of 14.3 dB with ±0.9 dB variation, 31.5 dB amplitude control range with 0.5 dB step, and a differential signal selectivity from 2 to 20 GHz. This single-tap circuit block is expected to enable a diverse set of tasks, including tunable RF front-end filtering, pre-linearization of signal for saturation compensation, and direct throughput matched filtering.
[Pathologo-anatomic and experimental study of the morphology of Q-fever].

: The article presents a survey of the literature containing reports on lesions of some human organs in Q-fever and on a possible role of the pathogene--Rickettsia burnetii in the development of these lesions. Pathoanatomical findings were compared with those of the experimental investigations of the Q-rickettsial infectious process. The literature data and the author's own investigations confirm the capability of Rickettsia burnetii of parasitizing in cells of the reticulo-endothelial system. This is an important factor in the pathogenicity of this causative agent and lies at the root of the pathogenesis of Q-rickettsial infection.
Cross-Domain Transfer Learning for Complex Emotion Recognition

Automatic recognition of basic emotions has improved the understanding of human emotions and has paved the path to understand complex emotions better, closer to real-world scenarios and having immediate applications. This paper investigates the utility of spectrogram (obtained from audio signals) images to perform complex emotion recognition by using transfer learning technique. Further, visual domain frameworks are adapted to audio frameworks. Transfer learning using pre-trained AlexNet are investigated. Information extracted from deep layers of the transfer learned networks was encoded as features and were trained on a linear classifier. Experimental results on EmoReact dataset consisting of 8 complex emotions shows the effectiveness of the proposed framework. Highest F1 score and AUC-ROC values as against the baseline performance by 16% and 10% respectively have been observed.
An Innovative Query Tuning Scheme for Large Databases

In today's competitive world businesses place more emphasis on information considering them as the more valuable asset of the organization. Hence data is given more attention than that is given to hardware or software and even sometimes to the human resources. The utility of data relies on the fact that how soon it can be turned into useful information and to strategic knowledge. Data is generally stored in a database that powers the company's information system. Relational databases store data in a series of related tables as rows and columns. When a query is issued seeking data from the database, it extracts data from multiple tables depending on the criteria set in the query and presents it as a single set of rows and columns. Hence, the response time of SQL queries becomes a visible factor for the performance of the entire information system. Therefore, query optimization becomes a vital function for preventing performance degradation. In this paper, the authors present two SQL tuning techniques that improve the performance of data retrieval from relational databases over non tuned SQL queries.
Research on Harmonic Distribution Characteristics and Resonance Degradation Phenomenon of Photovoltaic Power Station

A large number of photovoltaic access to the power grid will lead to new characteristics of grid harmonics due to the increase of the scale of converter equipment, and photovoltaic power stations are prone to generate harmonic interaction with the distribution network transmission lines to affect the power quality, so it is of great significance to study the harmonic distribution characteristics and resonance degradation phenomenon of photovoltaic power station. In this paper, the photovoltaic power station model is firstly established to analyze its harmonic output characteristics, and then the photovoltaic power station model is connected to different locations of the IEEE34 node test system to study the impact of different access locations on harmonic distribution. Finally, according to the resonance degradation mechanism of transmission lines, the variation of harmonic amplification coefficient and resonant deterioration degree of transmission lines with different distance and different harmonic frequency are studied. The effectiveness of the model is validated by simulation, and the harmonic distribution law and harmonic interaction factors of photovoltaic power stations are obtained, which provides a theoretical basis for harmonic control of photovoltaic power station.