Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Navigation in Rural Environment based on Digital Filters and LIDAR Data

Road detection is one of the essential problems in autonomous driving. Common approaches for road detection are heavily based on predetermined highly accurate digital maps. Such approaches work good in urban locations. Outside of them, in rural environment, building of such maps as well as their storing and transmission are very challenging tasks. Moreover, rural environment is known to vary rapidly due to natural and human factors. These factors limit the implementation of the state-of-the-art approaches suitable for urban environments. In this paper, we present the approach which performs road surface detection based on sensor perception system without detailed prior maps. We detect road borders on the obtained surface by computing the distance from a car to all surrounding points and process this data by digital filters and observation of distance dashes. After road detection calculation of car position relative to road borders is performed. Such approach allows generating feasible trajectories for the vehicles to reach path waypoints. The generated trajectories are updated taking into account vehicle odometry and IMU data. We demonstrate the performance of the approach on a full-scale autonomous vehicle navigating in rural environment. Obtained results proved the approach allows the vehicle to navigate in rural environment reliably and at a high speed.
Link Prediction Based on Orbit Counting and Graph Auto-Encoder

Link prediction aims to predict the missing edge or the edge that may be generated in the future. The key to link prediction is to obtain the characteristic information with strong representation for nodes. In this work, aiming at the problem that the existing link prediction methods fail to take full account of the high-order connection mode of nodes, we present a new representation learning-based approach called OC-GAE (Orbit Counting and Graph Auto-Encoder) that considers rich subgraph structure around nodes. Firstly, the number of orbits on subgraphs is calculated as the high-order structural features of the nodes; then, the number of orbits is used as the input of Graph Auto-Encoder to learn the efficient representation of the nodes; finally, the network adjacency matrix is reconstructed by the learned representation to realize the link prediction. By comparing with five classical link prediction methods and two mainstream network representation learning methods on four real network datasets, the effectiveness of the proposed method and the prediction accuracy are proved to be optimal in general.
A Nine-Level Inverter for Open Ended Winding Induction Motor Drive with Fault-Tolerance

This paper presents a nine level inverter for an open ended winding induction motor (OEWIM) drive with fault-tolerance property for switch faults. The proposed topology consists of three three-phase inverters with an isolated DC source for each inverter, three bi-directional switches and three capacitors. The three inverters are configured such that they all have a common neutral connection between them. Such configuration provides the advantage of producing peak output voltage twice the source voltage magnitude and hence lower rating voltage sources can be employed. Conventional sinusoidal pulse width modulation techniques are employed for generating gate pulses for the switches in the proposed topology for normal and post-fault operation. A fault-tolerance strategy is proposed for the post-fault operation of the inverter to produce balanced three phase supply. Simulations are carried out in MATLAB/Simulink environment and results are presented.
On the Development of a Resident Monitoring System: Usability, Privacy and Security aspects

Worldwide, the elderly have suffered disproportionately from the effects of the COVID-19 pandemic, both in terms of their prognosis once contracted the disease and in terms of the preventative measures required for this demographic, who are at much higher risk than the rest of the population. In the “new normal”, the well-being of older adults (residing either in their own homes or in care homes) will be ideally monitored remotely. These measures would preserve the independence of individuals without compromising on their safety. In this paper we discuss aspects of the design and implementation of a resident monitoring system (RMS) with particular emphasis on overcoming the barriers for adoption among these populations, by addressing the aspects of usability, privacy and security at the core of the development of such a system. We discuss the current challenges of this research and future work on the RMS.
Dynamic Neural Network-based System Identification of a Pressurized Water Reactor

This work presents a dynamic neural network-based (DNN) system identification approach for a pressurized water nuclear reactor. The presented empirical modelling approach describes the DNN structure using differential equations. Local optimization algorithms based on unconstrained Quasi-Newton and interior point approaches are used in the identification process. The efficacy of the proposed approach has been demonstrated by identifying a nuclear reactor core coupled with thermal-hydraulics. DNNs are employed to train the structure and validate it using the nuclear reactor data. The simulation results show that the neural network identified model is sufficiently able to capture the dynamics of the nuclear reactor and it is suitably able to approximate the complex nuclear reactor system.
Design of PV Emulator Fed MPPT Controlled DC-DC Boost Converter for Battery Charging

To extract maximum power from solar panel, it is essential to use different types of Maximum Power Point Tracking (MPPT) algorithms. But these algorithms need rigorous testing before it can be applied practically on Photovoltaic (PV) panel which is troublesome due to higher dependence on solar irradiance, large space requirements if power rating increases etc. These reasons lead to develop a PV emulator with acceptable performance which cost less than the commercially available PV emulators. In this paper, design of a PV emulator with resistive load is proposed to mimic the characteristics of real PV sources for different power rating and specifications. The design of the power and control stages has been validated by simulation results. Finally, the PV emulator is used for battery charging through MPPT controlled DC-DC boost converter. The results are compared with the real PV panel and it is found that they are in close agreement. This new testing bed is more flexible in terms of emulating different ratings and brands of solar panel. This portable PV emulator also provides controlled regulation in terms of irradiance and temperature which leads to more tunable optimization of MPPT controllers.
dentification of Suitable Modulation Scheme for Boosted output in ZSI

In order to obtain a sustainable alternative for the power generation in developing countries, grid coupled PV panels are the best solution which is widely preferred in developed countries. A PV module with a grid coupled inverter is embodied in an AC module. Transformer less single stage arrangements are highly efficient and preferred over different arrangements of an AC module. ZS I being one of such arrangements have seen a fast evolution since its first release in 2003, in which its different arrangements along with its control and modulation schemes are included. This paper work emphasizes on the performance of ZS I under three modulation techniques provided with constant input voltage and modulation index, and finally conclude with a suitable scheme to obtain high output voltage.
Implementation of Machine Learning Algorithms to Detect the Prognosis Rate of Kidney Disease

The chronic kidney disease is the loss of kidney function. Often time, the symptoms of the disease is not noticeable and a significant amount of lives are lost annually due to the disease. Using machine learning algorithm for medical studies, the disease can be predicted with a high accuracy rate and a very short time. Using four of the supervised classification learning algorithms, i.e., logistic regression, Decision tree, Random Forest and KNN algorithms, the prediction of the disease can be done. In the paper, the performance of the predictions of the algorithms are analyzed using a pre-processed dataset. The performance analysis is done base on the accuracy of the results, prediction time, ROC and AUC Curve and error rate. The comparison of the algorithms will suggest which algorithm is best fit for predicting the chronic kidney disease.
Probabilistic Modeling of Li-Ion Battery Remaining Useful Life

The degradation of lithium-ion batteries in smart grid and electric vehicle applications is a major drawback, since at some point in their life they become unable to meet minimum performance in terms of supplied voltage and power. Health prognostic is mandatory to ensure safe and reliable operation of batteries, as unsuccessful operation may cause technical/economic detriments or complete failures. The battery Remaining Useful Life (RUL) depends on several random factors and thus it should be probabilistically characterized to allow for decision-making processes. In this paper the Inverse Burr (IB) distribution is proposed to model the RUL, and we develop a hybrid methodology to characterize the RUL even with small amount of data. The IB distribution is applied on a RUL dataset created by Monte Carlo sampling on an electrochemical battery model fitted upon given charge/discharge cycles. Numerical experiments are reported to assess the proposal with respect to benchmark distributions.
Real Time Air Monitoring, Analysis and Prediction System Based on Internet of Things and LSTM

Air pollution is a serious threat to people’s health. Air quality monitoring and prediction has become an important task. We propose a real-time air monitoring and analysis and prediction system, through the Internet of things module real-time monitoring of the campus meteorological data, the air quality information of five prominent places in the campus is analyzed and predicted. Our monitoring system consists of communication module , temperature and humidity sensor, laser dust sensor, voltage detection module, cloud server and mobile application program. People can access applications or cloud data onto our devices to monitor air quality and weather data. In addition, we predict the air quality of the next 24 hours by the data onto cloud particles, and analyze the correlation between the detection data according to Pearson correlation coefficient. Finally, we verify the overall low power consumption of the monitoring system by electricity monitoring. Our system has completed the test of the campus. The experimental results show that the system can achieve the function of low-power data monitoring, analysis and prediction.
Analysis of Flexible Pavements Comprised of Conventional and High Modulus Asphalt Concrete Subjected to Moving Loading using Linear Viscoelastic Theory

In order to prevent distresses in asphalt pavement, the analysis of pavement structure should be as close as possible to its real behavior. However, in actual standard for designing asphalt pavement, viscoelastic behavior of asphalt mixtures and effects of moving vehicles are still not considered. In this paper, the authors aim at applying an analytical approach for analyzing the linear viscoelastic behavior of asphalt pavements comprised of conventional and high modulus asphalt concrete caused by a moving loading. It is shown that the effects of moving load give rise to an increase in tensile stress at the pavement surface and at the bottom of asphalt layers, which can be a reason for premature cracks in flexible pavement. The results also show that the use of high modulus asphalt concrete layers gives rise to a significant reduction of tensile stress and strain at the pavement surface as well as at the bottom of asphalt layer.
Video Sensor Security System in IoT Based on Edge Computing

Security is the most urgent problem in big data of video sensor networks. A security protection system is designed to promote the security and privacy of big data in edge computing called video sensor security system. In the system, an intrusion detection model based on deep neural network is proposed and implemented to monitor the traffic as a passive defense of the sensing layer on edge computing platform EdgeX Foundry. Camera pattern noise is used as a credential for authentication as the active defense of the sensing layer. The camera will not access the edge network until passing intrusion detection model verification and authentication which ensures that unauthorized users will not be accessed to EdgeX Foundry. In the double security protection, this security protection system has been proved to have good performance and high security.
Performance Analysis of a Full-Duplex MIMO Decode-and-Forward Relay System With Self-Energy Recycling

This paper analyzes the performance of the full-duplex (FD) multiple-input-multiple-output (MIMO) decode-and-forward (DF) relay system in the presence of self-energy recycling (S-ER). Specifically, this work proposes an antenna allocation scheme for S-ER as well as analytically evaluates the performance of this proposed scheme in terms of spectral efficiency (SE), energy efficiency (EE), outage and symbol error rate (SER). Since, the self-interference (SI) is a major bottleneck in achieving the true potentials of FD communication, this work considers SI as an energy harvesting opportunity to enhance the EE of the system. The proposed analysis shows that reserving few antennas for S-ER at the FD-MIMO relay improves the EE, however, it reduces the antenna array gain at the relay for information transmission and reception. Thus, a slight degradation in the SE, outage and SER is observed. Further, an adaptive S-ER technique has been proposed which utilizes the antennas corresponding to the least channel gain with respect to information transmission and reception for S-ER. This adaptive S-ER technique improves the EE and also compensates for the slight degradation in the SE, outage and SER through adaptive antenna allocation. Closed-form expressions for the SE, EE, outage and symbol error rate have been derived for these S-ER techniques. In the end, simulations results are provided to validate the efficacy of the theoretical derivations.
IoT-Based Laser-Inscribed Sensors for Detection of Sulfate in Water Bodies

This paper presents the fabrication of novel flexible sensors from metalized polymer films and their subsequent utilization for environmental applications. Polyethylene terephthalate films coated with thin-film of aluminum was used as a singular material to form the sensor patches. Optimization was done on the laser parameters to form laser-inscribed interdigitated electrodes on the aluminum side of the polymer films. The sensors were then used to detect the presence of sulfate ions in the water samples. Electrochemical impedance spectroscopy was used to detect the resistive and reactive changes with respect to the corresponding changes in the concentration of the tested solutions. Experiments were conducted using five different concentrations ranging between 0.1 ppm and 1000 ppm. The sensitivity, limit of detection and response time of the salts were $0.874~\Omega $ /ppm, 0.1 ppm and one second, respectively. The repeatability of the sensors was also tested to validate their responses for the target analyte. An optimal frequency was chosen to form an IoT-based system that consisted of an impedance analyzer AD 5933, Wi-Fi embedded Arduino and 2:1 multiplexer ADG849. The interfacing of the microcontroller-sensed data was also done with the cloud server to showcase the potentiality of the developed systems as portable devices for real-time applications.
Fractional order Modeling and Control of a Quadruple-tank Process

In recent years, fractional calculus attracts considerable attention from researchers in modeling and control of systems due to its flexibility in describing the behaviors of the dynamic systems. In this paper, a control design method based on fractional order will be proposed for a multi-input multi-output (MIMO) process. To deal with the interactions between system variables, the decoupling technique using simplified decoupling is adopted. However, in this paper, the PSO algorithm is proposed to approximate the complex decoupled transfer function into a simple form of fractional-order transfer function. The tuning rules of a fractional-order PID (FOPID) controller are also derived analytically based on the internal model control (IMC) structure. To validate the proposed method in real applications, the identification of a multivariable process (MIMO), the quadruple tank system, is presented by using the diagonal form of matrix fraction description (MFD) which transforms a MIMO process into multi-input single-output (MISO) sub-models. Then, the proposed controller is simulated as well as implemented based on the identified models to illustrate the effectiveness of the proposed method.
Analysis and Modeling of Galloping Robot With Actuated Tail for Balance*

In the rapidly evolving field of mobile galloping robots, there is a problem of stabilizing underactuated systems because they face an unknown dynamical environment during high-speed motion. Nevertheless, using the principles of biomimetics, we can implement a way to preserve a galloping robot’s equilibrium in the same manner as land animals with long tails do. Our task is to understand how the tail works to balance and turn in nature and transfer this to the construction, taking into account the mechanics and control system’s features and advantages. It is also highly important to solve the tail’s control problem, which should not complicate the entire robot’s control system. We considered the tail influences of two types of galloping robots: quadrupedal locomotion robot and one-leg hopper. Using the active tail, we can ensure that the galloping robot can move over uneven and unknown terrain without losing its balance even if there is an external disturbance. The study includes analytical modeling and simulation results.
Fuzzy-Based Trustworthiness Evaluation Scheme for Privilege Management in Vehicular Ad Hoc Networks

The vehicular ad hoc network (VANET) is a type of mobile wireless networks, where vehicles are allowed to broadcast a message to its neighbors and access data from other participants. However, how to guarantee the reliability of these broadcast messages and prevent malicious vehicles from accessing the private data of the VANETs is still an open problem to be solved. As a countermeasure, a fuzzy-based trustworthiness evaluation scheme for privilege management in VANETs is proposed in this article. In the proposed scheme, to ensure the result of trustworthiness is valid, mutual authentication with conditional anonymity between the evaluator and the vehicle to be evaluated is first employed. Then, based on the vehicle's behavioral big data, the trustworthiness of each vehicle is evaluated by utilizing the fuzzy theory. Note that the privilege of a vehicle and the reliability of the vehicle's messages are determined by its trustworthiness. Moreover, the mobility of vehicles is also considered in this article, since the location of a vehicle is not constant and the monitoring area of an road side unit is limited. The results of theoretical and experimental analyses demonstrate that the proposed scheme performs well in terms of security and efficiency.
Severe Analysis of Cardiac Disease Detection using the Wearable Device by Artificial Intelligence

In the current era of technology, Artificial Intelligence (AI) is playing a vital role in the health care sector especially cardiac disease detection which is a major cause of sudden death. Both the elderly and young are at the risk of sudden cardiac death at the ratio of 1–2% all around the world. Although AI technology with wearable technology is being used to detect heart diseases for quite some time now, sometimes it fails due to multiple reasons which include algorithm failure, high cost of treatment, limited battery time wearable device, data training issues, security and privacy issue in IoT, slow working of devices, poor internet or patients don't reach the hospital on time. Which gives rise to false results. Security and privacy issues in the old devices are the biggest flaws due to which old devices work slowly and the internet issues are common, it helps us to check their heart parameters anytime and anywhere in the world which reduces the hospital's workload, cost issues and to line onward. Meanwhile, these problems can be overcome by using modern models such as ECG assessment, AI-based guidelines, Visy's model which can recognize five critical diseases. A Wearable ECG patch is a very lightweight model that provides high accuracy and efficiency. These devices are trained by using a machine learning algorithm, and AI plays a prime role to detect the diseases. It helps us to check their heart parameters anytime and anywhere in the world which reduces the hospital's workload and cost issues, and the devices provide updated information as real-time data is stored online and secured with firebase authentication. It is concluded that all modern devices are more efficacious, cost-effective, user friendly, and more secure.
Leveraging Multi-view Learning for Human Anomaly Detection in Industrial Internet of Things

One of the most prominent human anomalous behaviour is fall. Fall detection for elderly care is important in the smart home care system. Fall detection is one of the smart home applications. In home-based assisted living, the detection of human anomalous behaviour is crucial. Home-based assisted living is becoming prevalent in elderly care. Aging brings several health issues and prevents the elderly to live an independent life. Therefore, it is important to detect human anomalous behaviour to ensure the safety of older adults. In this paper, we propose a multi-view learning approach to detect human anomalous behaviour. Our multi-view algorithms are running on edge devices. Multi-view learning is basically a method of learning from multiple views. It involves data fusion or integration from learned multiple feature sets. We evaluate our approaches for fall detection on the publicly available dataset “MobiFall_Dataset_v2. 0”. We construct 2 distinct views of accelerometer and gyroscope data and apply co-training methods to train the classifiers. We perform experiments on the extracted feature sets of two different views and combined them using 3 different methods. We introduce 3 feature fusion methods to leverage the inadequacy of single-view learning. Multi-view approach used in this work has an improvement over single view learning and produces better accuracy in classification of falls.
Drone Image De-Noising and Feature Extraction

Drones are very commonly used in many civil and commercial applications like precision agriculture, infrastructure inspection, firefighting, mining, search and rescue operations, logistics, film making etc. They are equipped with one or the other type of sensor to detect frontal obstacles. Cameras are the widely used sensing element in small, light weight drones. There are various applications with this low flying, light weight drones. In this paper, the two video preprocessing steps, Noise removal and Feature extraction which are performed as a primary step in obstacle detection has been experimented. Videos are shot using a low flying drone in outdoors. Various filtering and feature extraction techniques has been compared and their performance is evaluated.