Miracle-dz/UniFAR-FTUs huggingface dataset:
Robot-Aided Upper-limb Proprioceptive Training in Three-Dimensional Space

Proprioception, the ability to sense body position and limb movements in space without visual feedback, is one of the key factors in controlling body movements and performing activities of daily living. However, this capability might be affected after neural injuries such as stroke. Robotic platforms can be used to monitor and promote arm movements and, therefore, can assist in developing rehabilitation protocols that aim to improve proprioception through repetitive reaching motions without vision. The objective of this paper is to investigate if a robotic training protocol improves the end-position reaching proprioceptive sense in three-dimensional (3D) space. As an initial step towards clinical application, a robotic platform was employed to train the end-position proprioceptive sense in six healthy participants. During the training phase, volunteers used their dominant hand to reach without vision to two different targets in 3D space. Positions of these targets were carefully chosen to create a hand movement pattern similar to that used when self-feeding, which is an important activity of daily living. At the end of each training trial, participants were provided with visual feedback to help them move their hands to the exact locations confirmed through haptic feedback. Their performance was evaluated before and after the training in an assessment phase during which participants were asked to move from the start position to the same two targets as well as an additional third one without any visual or haptic feedback. The results from this study show significant improvements in overall reaching accuracy and trajectory smoothness, demonstrated by 41% decrease in the average end-position error and 13% reduction in the average index of curvature after the training. This research suggests the potential of designing robotic rehabilitation protocols for improving 3D proprioception.
Downlink Performance Evaluation of NOMA Systems Based on Different OFDM Waveforms

Non-orthogonal multiple access (NOMA) schemes have emerged as an inspiring technology for next generation mobile communication systems. This paper firstly confirms the superiority of NOMA systems over orthogonal multiple access (OMA) schemes by considering orthogonal frequency division modulation (OFDM) waveform. Further, it is verified that coded NOMA systems based on low-density parity-check (LDPC) coding outperform uncoded NOMA systems in terms of bit error rate (BER) performance metric with respect to transmit power. To show the performance of OMA and NOMA systems, two cells each having a single base station (BS) are constructed. For simplicity, two devices are considered in OMA cell while four devices are used in NOMA cell which utilize multiple user superposition transmission (MUST) technique (downlink power-domain version of NOMA). Later, performance of the system is analyzed in terms of throughput and peak-to-average power ratio (PAPR) for downlink scenario. Lastly, performance of the considered system for different waveforms such as OFDM, weighted over lap and add (WOLA) and filtered OFDM (F-OFDM) is compared. According to obtained simulation results, WOLA waveform generally outperforms OFDM and F-OFDM waveforms in throughput, however, slight better results are being shown by F-OFDM waveform in high PAPR values.
Evaluation of geodesign maps for spatial planning

This study investigated the maps of four geodesign tools to present stakeholder objectives. The tools vary in how objective value information is presented. The tools differ in the number of calculation steps and the complexity of these steps. The more complex tools are less suitable for interactive use, though these tools are suitable to support specific stakeholder tasks.
Investigation of Inter-User Interference Effect on 5G Uplink Communications

Manifold connected devices are expected in Fifth Generation (5G) wireless communication systems that may lead to serious interference issues among the User Equipments (UEs). This inter-user(s) interference certainly degrade the performance of devices in terms of Bit Error Rate (BER) and throughput. This paper investigates the impact of different modulation waveforms such as Orthogonal Frequency Division Modulation (OFDM), Filtered OFDM (F-OFDM) and Filtered Bank Multiple Carrier (FBMC) on the performance of devices in 5G uplink communications and sorts out which waveform provides optimal results in the presence of inter-user interference. To analyze the effect of inter-user interference, a general model is constructed consists of two cells each having single UE and a Base Station (BS) for simplicity. Pedestrian A channel model is chosen in this study, whereas Low-Density Polarity-Check (LDPC) coding scheme is considered for channel coding for all the considered waveforms. The performance of the system in terms of BER, throughput and Peak-to-Average Power Ratio (PAPR) for the aforementioned waveforms are analyzed through simulation studies. According to simulation results, FBMC waveform generally outperforms OFDM and F-OFDM in all the performance metrics (i.e., BER, throughput and PAPR).
A solitary approach to test path prioritization using significance of centrality measures

This paper utilizes the significance of three centrality measures of a graph - Betweenness, Closeness and Degree of node in prioritizing the test cases. With the help of DD graph and graph centrality parameters, we propose an Importance Factor. Significant decision points and independent paths in the graph can be determined by analysing the values of this calculated Importance Factor for each node which may serve as a basis for test case prioritization. Since DD graph can also be used to get inferences for entry and exit criteria while forming test plan in testing phase, the proposed method would help largely in mitigating the expenses and time incurred during the testing of the software.
Energy Internet: A Novel Vision for Next-Generation Smart Grid Communications

Energy Internet (EI) is a novel concept that can be thought of transformation of smart grids into the Internet where different energy forms can be integrated to provide more efficient and resilient power grid. Deployment of modern communication techniques are expected to serve as backbone for EI. However, designing advanced and robust communication infrastructures for enabling effective EI systems is a very critical and complex task. The key aim of this study is to provide an analysis to address present status of EI systems. Firstly, the key idea of EI systems along with its essential components is presented. Later on, latest developments in energy routers, information and network systems that are essential for concept of EI are discussed. At last, the threats and their possible solutions subjected to the EI systems are discussed.
Cascaded Predictive Direct Power Control for Grid Connected Photovoltaic System

Generally, the topology of a grid connected photovoltaic system (PV-GCS) includes two or more power converters placed in cascade and they are separately controlled requiring several controllers. In this paper, an optimal cascaded predictive power control strategy (CPPC) for PV-GCS is presented. The method considers the three cascaded power converters (Buck converter, Boost converter and a three phase Voltage Source Inverter VSI) as one stage system and controls them at the same time with the same optimized cost function, providing more flexibility and simple controllability and allows the system able to compensate the undesired grid reactive power. Also, the functioning at the maximum power point (MPP) is ensured by the same control method. The presented research is validated by detailed simulation and experimental tests, which confirm the suitability of the proposed method for PV-GCSs.
Reaching in Several Realities: Motor and Cognitive Benefits of Different Visualization Technologies

There is increasing interest in using virtual reality (VR) in robotic neurorehabilitation. However, the use of conventional VR displays (i.e., computer screens), implies several transformations between the real movements in 3D and their 2D virtual representations that might negatively impact the rehabilitation interventions. In this study, we compared the impact on movement quality and cognitive load of novel vs. standard visualization technologies: i) Immersive VR (IVR) head-mounted display (HMD), ii) Augmented reality (AR) HMD, and iii) Computer screen. Twenty healthy participants performed simultaneously a motor and a cognitive task. Goal-oriented reaching movements were recorded using an HTC Vive controller. The cognitive load was assessed by the accuracy on a simultaneous counting task.The movement quality improved when visualizing the movements in IVR, compared to the computer screen. These improvements were more evident for locations that required movements in several dimensions. We found a trend to higher movement quality in AR than Screen, but worse than IVR. No significant difference was observed between modalities for the cognitive load. These results provide encouraging evidence that VR interventions using HMDs might be more suited for reaching tasks in several dimensions than a computer screen. Technical limitations might still limit the beneficial effects of AR, both in movement quality and cognitive load.
A Survey on Feature Extraction Technique for Facial Expression Recognition System

This paper presents a broad study about various feature extraction approaches in the field of facial expression recognition (FER), in this whole paper the term FER refers to facial expression recognition. The recognition of the facial expression depends on the preprocessing stage of input image and on the preciseness of the feature extraction approaches, the preciseness of the feature extraction approaches plays an important role in FER so various techniques are proposed in the literature for feature extraction which enhances the accuracy of the feature extraction approaches for the identification of the facial expression, this paper present a summary for the various feature extraction approaches proposed by various authors and a conclusion is made about current trend in feature extraction technique and the future enhancement that can be made in this area.
A Phased Array Antenna Element Failure Diagnostic Method Using Independent Measurements of Different Phases

A phased array antenna element failures diagnosis method is introduced by concerning the phase information to provide abundant independent field measurements. The sampling of field data is performed during array scanning when the excitation phases are changed. A phantom-bit technique is employed to enhance the diversity of the sampling set. A nonconvex optimization method is used for sparse approximation to reconstruct the unknown elemental excitations.
Assessment of an On-board Classifier for Activity Recognition on an Active Back-Support Exoskeleton

Despite the growing interest, the adoption of industrial exoskeletons may still be held back by technical limitations. To enhance versatility and promote adoption, one aspect of interest could be represented by the potential of active and quasi-passive devices to automatically distinguish different activities and adjust their assistive profiles accordingly. This contribution focuses on an active back-support exoskeleton and extends previous work proposing the use of a Support Vector Machine to classify walking, bending and standing. Thanks to the introduction of a new feature-forearm muscle activity-this study shows that it is possible to perform reliable online classification. As a consequence, the authors introduce a new hierarchically-structured controller for the exoskeleton under analysis.
Deep Learning and its Importance for Early Signature of Neuronal Disorders

Deep learning is a nomenclature given to a recently developed domain of pattern recognition. It has potency to influence the development of artificial intelligence research. The concept of deep learning has been originated from the capability of making educated decisions/guesses regarding a topic/situation in spite of having no prior experience. From mathematical perspective, deep learning allows the extraction of features from a given set of data which allows much more capable and accurate training for a certain technique. With the advent of more powerful mathematical techniques deep learning is seen as an effective method to enhance the capabilities of presently developed algorithms without needlessly complicating their mode of operation. This paper presents importance of deep learning to detect neural disorder.
Class E Resonant Converter Design for LED Drivers

The LED (Light Emitting Diode) lighting technology has been becoming widespread rapidly due to having advantages such as extremely long lifetime, compact structure and very low energy consumption. In the literature, various LED driver topologies are proposed for controlling the LED's current and voltage. However, most of them require a large capacitor and inductor values due to low switching frequency operation. On the other hand, high capacitance with low volume can be achieved by using the electrolytic type capacitors which limits the lifetime and reliability of LED driver. By the help of high switching frequency, converter size can be reduced significantly. In addition, the capacitor size decreases and therefore electrolytic capacitors can be eliminated. In this study, isolated Class E resonant topology operating at 1 MHz is analysed and designed for 48 V/60 W offline LED driver. The design methodology is presented and verified by PSIM simulation.
S Teleoperation System Based on 1-DOF Pneumatic Maipulator

Paper presents a novel approach to a control design of bilateral teleoperation systems with force-feedback. The problem statement, analysis of research achievements to date, and the scope of the study are presented. The new design of a control unit for a master-slave system with force-feedback is presented on a simple and ideal 1-DOF bilateral teleoperation system. System control unit was based on an inverse model. The model was used to reduce value of force in the force-feedback communication channel, that the system might generate in freemotion. A substantial part of the paper is focused on the development of a mathematical model covering phenomena occurring in the investigated control scheme. The new approach was validated on a test-stand of a rotating non-linear pneumatic manipulator arm. Two linear pneumatic actuators were used in the drive system. The paper presents the modeling procedure of the experimental setup and the model used in the study. Three experiments are described to demonstrate the new control approach to master-slave objects with force-feedback. The paper contains conclusions regarding the control system and the experimental setup.
Impact of Solar Flares on HF Radio Communication at High Latitude

An increase in the solar magnetic field gives rise to the number of visible sunspots in the Earth facing side of the Sun. This tends to impact the Earth’s magnetic field. Occurrence of a geomagnetic storm depends on the interaction of magnetic fields of both the Sun (captured in solar wind) and the Earth. The energy of the sun is given out in the form of high-speed solar wind, solar flares, coronal mass ejections (CMEs), solar uV light and energetic particles (MeV). The effect of such geomagnetic storms is borne by the trans-ionospheric message carrying signal and affects both satellite navigation and radio communication systems on Earth. Moreover, the ionosphere, being a refractive and dispersive medium, adds as a source of error to the message signal. Thus, it becomes important to study the effect of space weather activities on GNSS signal and try to overcome its impact. This paper studies the impact of one such solar storm in the mid-high latitude region during the winter solstice of 2015 on GPS and high-frequency airplane communication. A case study of an event that occurred in Stockholm, Sweden on the 4 November 2015 has been investigated using the geomagnetic and satellite geometric parameters, and the total electron content (TEC) variation in the ionosphere. The analysis undertaken in this paper demonstrates the importance of understanding the serious threats solar activity poses to GNSS and its related applications on Earth, which can have rippling effects on industries which are evidently reliable on space technology.
Load Frequency Control with Considering Impact of Wind Turbine Using a Fractional PID Controller

Rising the demand for highly reliable electrical energy has increased the need to supply energy close to the load. Wind turbines are one of the most widely available sources of the distributed generation (DG). However, the unpredictable and oscillating nature of the wind causes fluctuations in wind turbine output power and frequency of the system. In this paper, a fractional PID controller is designed to mitigate the output power variations of the wind turbine. The controller is tuned using particle swarm optimization methods. The system is simulated in the Simulink environment of MATLAB software. The simulation results verify the effectiveness of proposed controller in damping power oscillations.
LoRa Based Smart Irrigation System

World’s total population has come to past 7.2 billion and the population rate is expanding step by step, in following 25 to 30 years there will be a major issue of food scarcity, so the improvement of farming is vital. Today, the farmers are experiencing the absence of downpours and shortage of water. The fundamental goal of this paper is to give a smart irrigation for saving time, cash and power of the farmer. The prototype uses Two ESP32TTGO LoRa boards in which one is placed in the field connected to sensors like temperature, soil moisture and water flow and the other is placed within the range of 5KM which is connected to the internet using the WiFi protocol of ESP 32 and thus the data is published onto the IBM Bluemix. The Data is analyzed to trigger the water pump automatically to pump the water into the fields. The trigger can also be given manually.
Parameters Extraction for Equivalent Circuit Model Based on Artificial Intelligence

In this paper, a novel parameter extraction method for equivalent circuit based on artificial intelligence (AI) technique is proposed. The Y-parameters calculated by UltraEM®, which is a high efficient electromagnetic simulation tool, are used as training data to establish corresponding AI model. With this AI model, the parameters of the equivalent circuit can be efficiently obtained by optimization method. An example of a spiral inductor is given to demonstrate the extraction procedure and validate the results.
Doppler Radar Techniques for Vital Signs Detection Featuring Noise Cancellation

Remote or noncontact vital signs detection through microwave and millimeter-wave radar techniques based on micro-Doppler effects can find wide-ranged applications in healthcare and biomedical sectors. Although a significant research progress has been achieved, there are still numerous challenging issues that should be addressed for practical implementations such as random body movement (RBM) and radar self-motion (RSM) artifacts which significantly degrade the detection accuracy. To cancel noises caused by RBM and/or RSM, innovative hardware technologies and advanced signal processing methods should be devised and developed. This paper reviews the recent progress of Doppler radar systems for noncontact vital signs detection and measurements featuring RBM and RSM noise cancellation.
Specialized Computer System Controlling the Technological Parameters of the Drilling Rig

Systematized and researched characteristics of quasi-stationary oil and gas objects complex on the example of industrial drilling plants. As a result, requirements for the implementation of algorithms and special processors for performing diagnostic functions of quasi-stationary objects are formed. The algorithm of identification of states of quasi-stationary objects on the basis of special codes is developed. The technique is proposed besides the compact coding of a set of technological parameters of drilling states in the form of structured codes that carry information about the number, time and planned economic data on the example of drilling processes.
Design of Multimode Triplex Antenna with High Isolation

A multimode triplex antenna with high isolation operating in the X-band is designed in this paper. The proposed antenna consists of a triangular loop, a coplanar waveguide and two tapered microstrip feedlines. Taking advantages of the triangular loop, high isolation performance is achieved in the proposed antenna. And, three different modes are excited by the coplanar waveguide and two tapered microstrip feedlines. Consequently, a multimode triplex antenna is obtained with high isolation. The optimized and measured results show that the operating frequencies of the proposed antenna are 10, 12.8 and 11.58 GHz, which are corresponding to three modes, respectively. Moreover, the isolations between each port are higher than 20 dB, and the corresponding bandwidths are 20% (8.94 GHz to 10.92 GHz), 20% (12.1 GHz to 14.6 GHz), and 12% (11 GHz to 12.45 GHz), respectively. The radiation gains are measured as 3.8, 3.3 and 3.3 dBi at three working frequencies, and cross-polarizations are 22dB, 18dB, and 16dB, respectively.
Double-Loop Fractional Integral Sliding Mode Trajectory Tracking Control for an Underwater Manipulator

A double-loop fractional integral sliding mode control (DLFISMC) strategy is designed to solve the trajectory tracking problem of an underwater manipulator with bounded external disturbance, which is composed of the outer loop position control and the inner loop velocity one. On the basis of the fractional theory and the stability method, the sliding mode surfaces and the reaching laws are designed respectively. In addition, an auxiliary is introduced to the inner loop controller to estimate the external disturbance. The Lyapunov theory can ensure the finite-time convergence and stability of the closed-loop system. The simulation results from a 6 degree of freedom (DOF) underwater manipulator validate the higher precision tracking performance and better robustness of the proposed control system against the external disturbance.
Detecting and Reporting Forest Fire through Deployment of Three Dimensional Multi-sink Wireless Sensor Network

Forest fires are common in the winter months across the world. An apt forest fire detection technique is required to check the spreading of fire and reduce the degree of loss thereby. In this paper we have proposed an energy efficient forest fire detection and reporting technique through three dimensional deployment of multi-sink wireless sensor network. The scheme may be used in event driven, time driven or a proposed hybrid mode. The results of the proposed scheme have been compared with 3 dimensional versions of some of the very well-known routing protocols for sensor networks.
Deep Convolutional Neural Network for Dot and Incipient Ridge Detection in High-resolution Fingerprints

Automated fingerprint recognition using partial and latent fingerprints employs level 3 features which provide additional information in the absence of sufficient number of level 1 and level 2 features. In this paper, we present a methodology for detecting two level 3 features namely, dots and incipient ridges. Specifically, we have designed a deep convolutional neural network which generates a dot map from the input fingerprint image. Subsequently, post-processing operations are performed on the obtained dot map to identify the coordinates of dots and incipient ridges. The results of our experiments on the publicly available PolyU HRF database demonstrate the effectiveness of the proposed algorithm.