Miracle-dz/UniFAR-FTUs huggingface dataset:
A Secure Authentication Mechanism for Multi-Dimensional Identifier Network

With the development of future Internet architecture research, the authentication mechanism has become one of the hot topics. However, the current authentication mechanism relies on certificate systems, which have security risks and affect network performance. At the same time, these mechanisms are designed for specific networks and can't authenticate various network entities. The unified naming strategy in the Multi-Dimensional Identifier Network (MDINet) provides the possibility for a universal authentication mechanism for various network entities. In this paper, we proposed a secure self-authentication mechanism based on MDINet. We designed a secure access authentication process and a concise terminal authentication process using Combined Public Key(CPK) cryptosystem. We implemented the mechanism and evaluated its performance in the prototype system. The experimental results show that our mechanism can guarantee the security of authentication in the case of large-scale terminals without significantly affecting network performance.
Improved iterative error analysis for endmember extraction from hyperspectral imagery

Automated image endmember extraction from hyperspectral imagery is a challenge and a critical step in spectral mixture analysis (SMA). Over the past years, great efforts were made and a large number of algorithms have been proposed to address this issue. Iterative error analysis (IEA) is one of the well-known existing endmember extraction methods. IEA identifies pixel spectra as a number of image endmembers by an iterative process. In each of the iterations, a fully constrained (abundance nonnegativity and abundance sum-to-one constraints) spectral unmixing based on previously identified endmembers is performed to model all image pixels. The pixel spectrum with the largest residual error is then selected as a new image endmember. This paper proposes an updated version of IEA by making improvements on three aspects of the method. First, fully constrained spectral unmixing is replaced by a weakly constrained (abundance nonnegativity and abundance sum-less-or-equal-to-one constraints) alternative. This is necessary due to the fact that only a subset of endmembers exhibit in a hyperspectral image have been extracted up to an intermediate iteration and the abundance sum-to-one constraint is invalid at the moment. Second, the search strategy for achieving an optimal set of image endmembers is changed from sequential forward selection (SFS) to sequential forward floating selection (SFFS) to reduce the so-called "nesting effect" in resultant set of endmembers. Third, a pixel spectrum is identified as a new image endmember depending on both its spectral extremity in the feature hyperspace of a dataset and its capacity to characterize other mixed pixels. This is achieved by evaluating a set of extracted endmembers using a criterion function, which is consisted of the mean and standard deviation of residual error image. Preliminary comparison between the image endmembers extracted using improved and original IEA are conducted based on an airborne visible infrared imaging spectrometer (AVIRIS) dataset acquired over Cuprite mining district, Nevada, USA.
Integral Nonlinear Dynamic Inversion based Control of Input-Derivative Affine Systems

In recent times, development of economical yet powerful microprocessors, and increasing demands on performance characteristics have spurred great advancements in the field of nonlinear control. However, most of the nonlinear control theory and its techniques are developed for input affine systems, whereas the dynamics of many physical systems depends not only on the inputs but also on their derivatives. While this phenomenon can be easily catered for in linear systems through either state-space or transfer function representations, it is not as straightforward for nonlinear systems. In this paper, it is shown that Input-Derivative Affine (IDA) systems can be transformed to remove explicit dependency on input-derivative, but the resulting system is generally non-affine with respect to input. It is a difficult proposition to accrue nonlinear control for a non-affine system. In this paper, a novel Integral Nonlinear Dynamic Inversion (i-NDI) based control scheme is presented to deal directly with IDA systems. An example of Twin Rotor MIMO system, having strong coupling between pitch and yaw dynamics, is illustrated to demonstrate the efficacy of the presented scheme.
A Novel Multi-Band Integrated Antenna Design in 5G Full Screen Mobile Phone

A multi band integrated antenna scheme for the fifth generation (5G) mobile phone is proposed, that is, one antenna integrates five bands, namely Global Positioning System (GPS) Level 1 (L1), GPS Level 5 (L5), Wi-Fi 2.4G, Wi-Fi 5G and Long Term Evolution (LTE) band 32, the antenna volume for the whole mobile phone is effectively saved. This five-in-one antenna is located in the upper left corner of the back of the mobile phone, the upper clearance and side clearance of mobile phone are 1mm and 0.2mm respectively. The size of five-in-one antenna is 38.5mm by 26mm. The form of five-in-one antenna is combining inverted F antenna (IFA) and parasitism. This antenna is radiated by metal frame and laser direct structring (LDS), measurement results show that, passive efficiency in free space of GPS L1, GPS L5, Wi-Fi 2.4G, Wi-Fi 5G and LTE Band 32 could reach -4.5dB, -8.5dB, -6dB, -4.5dB, -6.5dB respectively, also the specific absorption rate (SAR) value of the transmission band is lower than Federal Communications Commission (FCC) specification. A mobile phone equipped with this five-in-one antenna has been commercially shipped.
Adaptive Finite-Time Control of Teleoperation Mobile Robot with External Disturbance and Uncertainty

This paper investigates a finite-time control method that uses an adaptive fast nonsingular terminal sliding mode. The designed controller improves the trajectory tracking performance and the accuracy of the force reflecting for the teleoperation mobile robots with unknown environment. Based on the proposed control method, the finite-time tracking problem of the teleoperation mobile robot is divided into two layers, which are the finite-time trajectory tracking problem for the mobile platform and the manipulator mounted on the mobile platform, for analyzing. Furthermore, the stability of the teleoperation mobile robot system is guaranteed according to the Lyapunove function theory and terminal sliding mode control. Finally, the effectiveness of proposed algorithm was verified by several simulations.
Intelligent Space Robots Integrated with Enhanced Information Technology and Development Activities

This work presents the most recent research in Intelligent Intervals, which combines modern information technology and advanced robotics. Researchers use robots outfitted with various sensors and instruments on the surface of other planets, in orbit, or as aides to astronauts. The ability of space robots to move, work, and intelligently explore without needing to be touched on a frequent basis or relying heavily on human operators is important to their use. This necessitates the ability to comprehend the chaotic or unfamiliar environment in which people find themselves. In such a scenery, intellectual thinking about perception is required to carry out actions in a meaningful and consistent manner. As a result, the effectiveness of robotics and automated space systems in space exploration is intimately related to robot intelligence or autonomy. The Smart Space initiative has evolved in scope and complexity since its inception in 1996. The core of a smart space is a ubiquitous sensor network integrated into the human living environment that can track a large number of people and other objects in space. With the help of intelligent space, mobile robots operate in human living areas and provide a variety of services, such as human direction. Robots are becoming more capable of solving tough space resources and planetary surfaces with autonomy and prowess as space agencies throughout the world improve their robotic missions, proving their value and usefulness in every mission. The demand for this capability is increasing as international space agencies need humans and intelligent robots.
Computer Vision Extended Perception System for Blind People

There is a considerable number of visually impaired people in the world, who live their daily lives with limitations in their mobility due to the little information they have about their environment. This project proposes to use embedded systems to develop an assistance system for the blind, which, through machine learning models, describes the objects closest to the user and if they are known or unknown. To do this, two trained Machine Learning models will be used, the first to detect common objects and the second to identify particular objects. The intention of using both networks is to improve the accuracy of the system, by first detecting objects or people in an image and then classifying them with known labels (names or some identifier). The results obtained show the benefit of using these neural networks for the recognition of objects in the environment from a general database and then with a personalized one. Finally, to indicate the identified objects to blind people, the text of their labels is translated into speech.
The Impact of Surface Cleaning and Watering on Photovoltaic in Bangka Belitung Islands

The use of photovoltaics as a new renewable energy source is one of the options to increase the energy mix in Indonesia, especially in the Province of the Bangka Belitung Islands. In this regard, research was conducted to increase the efficiency of the photovoltaic used. The treatment is cleaning the surface of the photovoltaic panel and sprinkling water in front and behind the photovoltaic panel. From the measurement results, it was found that the measured voltage before cleaning on the photovoltaic surface was 10.1 - 14.5 Volts and after cleaning the photovoltaic surface increased to 13.4 - 18.3 Volts. The current measured before cleaning the photovoltaic surface is 0.68 - 3.2 Ampere and after cleaning the photovoltaic surface it becomes 2.2 - 8.9 Ampere. Then by treating the water sprinkling in front and behind the photovoltaic simultaneously, the lowest measured voltage is 14.3 Volts and the highest is 18 Volts. For the current measured after watering in front and behind the photovoltaic, the lowest measured current value is 3.2 Ampere and the highest is 9.2 Ampere.
Investigations on Improving Drivers' Safety using Artificial Intelligence and Internet of Things

Throughout the world, many road accidents are the result of drunkenness and reckless driving, speeding, poor driver health and drowsiness. The main idea of this product is to prevent road accidents. Therefore, this product contains sensors such as an alcohol detector, heart rate sensor, and temperature sensor to detect the driver's physical condition and use certain algorithms such as the Viola-Jones algorithm and other formulas, and agencies such as Open-CV, Dlib. , to get the driver's attitude. The alcohol sensor will detect whether the driver is drunk or not. The speed sensor will detect the speed of the car. The heartbeat and temperature sensor will detect the heart rate and body temperature of the driver respectively. Based on that, in various sensory studies, the system decides in favour of the driver. Also, this product detects driver drowsiness based on the input (driver's face and head), and includes software components for facial detection, and eye condition (open/closed). In this process, the condition of the car and the driver (physically and mentally) can be monitored using the Android system such as how drunk the driver is, the speed of the car, the location of the car, the healthof the driver and the driver drowsiness.
A Novel Countermeasure for Selective Forwarding Attacks in IoT Networks

As the Internet of Things (IoT) devices become more widespread there are rising public concerns about whether or not IoT devices and their services are secure. One of the major threats they face is selective forwarding attacks performed by malicious nodes. Although packets can be lost inherently due to network conditions, malicious nodes, such as those performing blackhole attacks, may deliberately drop some, but not all of them. Therefore, distinguishing these nodes from legitimate ones is not so easy. This study has proposed a lightweight countermeasure to deal with this kind of attack in IoT networks, using the standard IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). The mechanism is based on Mobile Trusted Nodes (MTNs). For the given threat model, we showed that our model has robust detection accuracy and brings no additional overhead to the network.
Use of Attention-Based Neural Networks to Short-Term Load Forecasting in the Republic of Panama

One pillar of our society is the use of electricity as an engine of development, Short-Term Load Forecasting (STLF) contributes to the resilience and security of electrical supply, by predicting the amount of electricity that should be generated in the near future. Humanity is currently moving from an energy mix based on fossil fuels to a sustainable energy mix, a green one. One challenge of this shift is to forecast, as accurately as possible, the amount of energy load at any moment. This study compares STLF performed by state-of-the-art Neural Network and SARIMA model. First, demand is predicted with SARIMA model and then with a neural network with attention, in this occasion, the Temporal Fusion Transformer (TFT), next, both techniques are compared. The results show that SARIMA is suitable for STLF, with average performance metric values of MAPE and RMSE, of 0.064 and 101.4 MWh, respectively; when use TFT, prediction accuracy increases with a MAPE of 0.044, and RMSE of 69.2 MWh. This research is presented as a review of the state-of-the-technique and thus establishes a baseline that can be used to forecast National Energy Load in the Republic of Panama.
A Tool for Home Monitoring in Parkinson's Disease

Parkinson's Disease (PD) is one of the most common degenerative diseases, characterized by motor problems of that develop as the illness progresses. This study, through the use of a method based on the use of three wearable inertial measurement unit (IMU) sensors, placed respectively on the feet and on the lower back, proposes a objective tool for the disease evaluation. Moreover, this system allows to a continuous monitoring in the home scenario during the daily activity of the subjects. In this study 28 volunteers were involved for the test (21 healthy subjects and 7 PD subjects). An algorithm capable of analyzing the data acquired by the IMUs has been implemented. The developed algorithm discriminate the walking periods and extract useful parameters for gait analysis. The results show an objective evaluation of the gait and turn parameters and a correlation between the drug administration and the ON and OFF states.
Implementation of a HITL-Enabled High Autonomy Drone Architecture on a Photo-Realistic Simulator

In the development process of Unmanned Aerial Vehicles (UAVs), researchers have to solve a lot of complex problems such as unreliable pose estimation, sensor errors, unknown and fast-changing environments, which require a lot of test trials. However, real-world testing of UAVs is expensive, time-consuming, and requires a pilot and careful testing of the correct integration of the hardware and software components. As a result, the usage of simulation tools presents itself as a promising alternative to real flight tests. Therefore, having a nearly photo-realistic, Hardware in the Loop (HITL) enabled and accurate simulation tool integrated with a popular autopilot board would be advantageous to the research community. We propose an implementation of a high autonomy drone navigation architecture integrated with the photo-realistic environment simulator Flightmare Renderer, the flight-dynamics simulator Gazebo and a popular autopilot board Pixhawk (PX4) controller to make a photo-realistic and accurate dynamic simulation for drones. By means of our simulation, we evaluate the drone system architecture by running a full set of algorithms required to achieve full autonomy, which includes visual-inertial pose estimation, mapping, path planning, obstacle avoidance, and control modules. That means each individual module and the whole system are tested and evaluated on the photo-realistic rendering and accurate flight-dynamics simulation environment. Thanks to the integration of the PX4 autopilot with HITL simulation, the progress from simulation flight to real flight experiments is easier and faster.
Estimation of Variance and Covariance Components in Linear Models

Abstract We write a linear model in the form , where is an unknown parameter and ξ is a hypothetical random variable with a given dispersion structure but containing unknown parameters called variance and covariance components. A new method of estimation called MINQUE (Minimum Norm Quadratic Unbiased Estimation) developed in a previous article [5] is extended for the estimation of variance and covariance components.
The General Carrier-based PWM Implementation based on Space Vector Analysis for Three-phase Three-level T-type NPC Inverter

The three-phase three-level T-type NPC (3LT2NPC) inverter is a good alternative to conventional NPC inverter due to it can be specified as the elimination of the diode requirement. This paper comprehensively evaluates the general carrier-based PWM (CBPWM) schemes on 3LT2NPC, regarding switching loss, neutral point balancing and harmonic distortion. The analysis of the proposed CBPWM method is based on the space vector analysis approach, where the relationship between CBPWM and space vector pulse width modulation (SVPWM) methods is described. Different CBPWM methods are built according to the distribution of zero vectors and redundant small vectors in the switching pattern of the SVPWM method. In this paper, a prototype of 3LT2NPC is built and tested, where both of SiC and Si Mosfet are used. The simulation and experimental results are provided to validate the feasibility of the proposed method.
The influence of the strain‐induced electric field on the charge distribution in GaN‐AlN‐GaN structure

We show that strongly pronounced piezoelectric properties play a key role in GaN‐AlN‐GaN semiconductor‐insulator‐semiconductor (SIS) and related structures. In sufficiently thin AlN layers, the lattice constant mismatch is accommodated by internal strains rather than by the formation of misfit dislocations. These lattice‐mismatch‐induced strains generate polarization fields. We demonstrate that, in a GaN‐AlN‐GaN SIS structure with the growth axis along a (0001) crystallographic direction, the strain‐induced electric fields can shift the flat band voltage and produce an accumulation region on one side and a depletion region on the other side of the AlN insulator. On which side of the insulator the accumulation region is produced depends on the type of atomic plane at the heterointerface (Ga or N). The surface charge density caused by the piezoeffect is on the order of 1012 cm−2. As a consequence of the asymmetry in the space charge distribution, the capacitance‐voltage (C‐V) characteristics of the SIS stru.
Reconfiguring an RFET Based Differential Amplifier

Recently emerged reconfigurable field effect transistors (RFETs) come with the advantage of dynamic polarity change at runtime. While this effect has been extensively analyzed for the use of digital cells already, the analog domain can benefit greatly from dynamic characteristic changes of field effect transistors (FETs), too. This work presents various views on an RFET based differential stage circuit from the perspective of reconfigurability. Approaches include variations in bias current and transistor gain, which influence differential gains and output swing. Separate polarity bias of the two differential RFET allows for offset adjustment and cancellation without clocking. The resistance of the active load transistors can be modulated by their polarity gate and transform the differential stage to a single balanced differential mixer.
Deep Learning System for Detecting the Diabetic Retinopathy

Retinal blood veins of an eyes are destructive directly by DR that is Complexity of diabetes. Firstly, it becomes problematic for an eye vision. However, if it develops on serious condition, it can badly affect for both eyes and moreover, it can damage the eyesight partly or completely. Primarily it happens because of the blood having excess sugar level. Thus, the diabetic patient is at high risk to attain such disease who has diabetes sugary constantly. Primary detection of this disease can totally discourage the likelihood of blindness. Therefore, well-organized system for screening is mandatory. This procedure reflects the deep learning practice particularly densely connected conv. network DenseNet-169 that is functional for primarily diagnosis the DR. Pics are ordered on the basis of their harshness level i.e. NDR (Nil Diabetic Retinopathy), MDR (Mild DR), MoDR (MoD. Diabetic Retinopathy), Severe & PDR. Kaggle is the source of data for this process to driveDR detection. The recommended method consists of several stages including Data info/collecting, info/preprocessing, augmentation of data and modelling/demonstrating.
Unmanned Autonomous Aerial Refueling Control Strategy Design and Test Evaluation

Autonomous aerial refueling could improve the range, endurance and deployment capability of UAV, and its control strategy is the key factor to ensuring the success of UAV aerial refueling. Hence, it is quite necessary and crucial to develop an effective control strategy for autonomous aerial refueling. In this paper, the research status of autonomous aerial refueling for UAV was firstly reviewed, and the differences between manned-aircraft and UAV in aerial refueling were also introduced. Then, a feasible design method of optimal route for autonomous aerial refueling was proposed in light of Dubins Curve. Furthermore, a control strategy of autonomous aerial refueling in the rendezvous and formation stage was raised, including the corresponding mission instructions and control laws. The strategy was both verified through simulation and flight test, which was designed and fulfilled by two small UAVs. The results showed that the proposed control strategy was reasonable and reliable, which would provide technical support for autonomous aerial refueling in the future.
Blow-Up Phenomena for a Singular Parabolic Problem

ABSTRACT We study blow-up results for generalizations of the corotational harmonic map heat flow from the unit disk into the unit sphere. The generalizations include more general targets, composition with certain holomorphic maps, and cases where the associated Dirichlet integral does not admit a finite lower bound. In the latter case, blow-up events are irreversible.
Design and Analysis of Low Bias, Low Phase Noise Photodetectors for Frequency Comb Applications Using Particle Swarm Optimization

We use particle swarm optimization to optimize the design of a low-bias (5 V) modified uni-traveling carrier photodetector for low-phase-noise frequency comb applications. We analyze the best design generated by the algorithm to determine the physics that leads to a phase noise reduction. We find that the phase noise can be reduced by increasing the electric field in the photon absorption region and by adjusting the doping levels and thicknesses of the layers in the intrinsic region to optimize the electric field.