Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety and Security oriented design for reliable Industrial IoT applications based on WSNs

Internet of Things based technologies are enabling the digital transformation in many sectors. However, in order to use this type of solutions, such as wireless sensor networks, in scenarios like transport, industry or smart cities, the deployed networks must meet sensible safety and security requirements. This article describes a Wireless Sensor Network design that applies multi-layered mechanisms and tools to ensure security, safety and reliability while maintaining usability in Rail and Industrial IoT scenarios. The proposed solution provides guidelines for choosing the best implementations given usual restrictions, offering a modular stack so it can be combined with other solutions.
Distributed Containment Control of Multi-Agent Systems with Velocity and Acceleration Saturations

This paper studies the problem of distributed containment control for multi-agent systems modeled by double integrators. A new class of rotation-free distributed containment controllers with successive saturated loops is proposed for the challenging situation where the velocity and acceleration of each agent are required to stay within their desired ranges. It is proved that the objective of containment control is achievable if the parameters of the controllers satisfy a mild condition. Moreover, the desired ranges of the accelerations and the velocities are allowed to be very small, and the interconnection topology is allowed to be directed and switched.
Distributed Trajectory optimization Method for Multi-UAV Formation Rendezvous over Directed Networks

This paper proposes a distributed trajectory optimization method for multiple unmanned aerial vehicles’ (UAVs) formation rendezvous problems. A trajectory optimization problem that minimizes the energy consumption and requires simultaneous rendezvous is established. To solve this problem distributedly, it is decoupled into a master problem and several subproblems. Each UAV solves the subproblem independently, and UAVs cooperate over directed networks to optimize the master problem using a proposed distributed subgradient algorithm. Furthermore, the convergence analysis of the proposed distributed subgradient algorithm over directed graphs is provided. Finally, simulation result shows the effectiveness of the distributed trajectory optimization method.
Finite-Time Synchronization of Nonlinear Multi-Agent Systems with Prescribed Performance

In this paper, we address the finite-time synchronization tracking problem for unknown high-order nonlinear multiagent systems with prescribed performance under a directed graph. The agent is described by an n-order normal form system with unknown dynamic and an external disturbance. The prescribed performance means that the synchronization tracking error preserves within a prescribed region, whose boundary functions are chosen as exponentially decaying functions of time. A tan-type barrier Lyapunov function is employed to guarantee that the tracking error stays always within the prescribed region. A finite-time control scheme is incorporated into the control design to achieve finite-time convergence of tracking error. That is, the tracking error is forced to converge to a predefined radius centered at zero in finite time. To improve the robustness of the control systems, neural networks are employed to approximate the unknown dynamic and a decentralized parameter estimator is applied to estimate the upper bound of the external disturbance. Based on backstepping technique, barrier Lyapunov function, and finite-time control scheme, a decentralized adaptive controller is proposed to guarantee that the tracking error converges exponentially to a small neighborhood around zero in finite time with guaranteeing prescribed transient performance. An illustrative example is given to show the effectiveness of the decentralized cooperative controller.
Designing Test Methods for Running Capabilities of Ground Robots for Nuclear Disaster Response*

This paper describes the development of some test methods for the robots utilized for nuclear disaster response and decommissioning tasks. From the experiences in the disaster response at Fukushima Daiichi Nuclear Power Station(FDNPS), we have learned the importances of the development of the robots and the improvement of the operation capability. In this paper, we describe the test methods for running performances of the robots that are fundamental functions for the tasks in the nuclear disaster response. As the typical examples, we describe the tests for the performance of running through the narrow passage, climbing up and down the stairs and running with dragging the cable.
Clutter Suppression Approach for End-Fire Array Airborne Radar Based on Adaptive Segmentation

End-fire array antenna is a kind of special radar antenna with beam pointing perpendicular to the normal direction of the array. Because of its low profile and directional radiation characteristics, it is especially suitable for blind zone compensation of airborne radar. However, there are few publicly available pieces of literature on clutter suppression for end-fire array radar. In this paper, we firstly simulate and analyze the clutter spectrum characteristics for end-fire array airborne radar (EAAR), and then propose a clutter suppression method for EAAR based on adaptive segmentation. In the proposed method, the concept of Riemann mean distance of temporal covariance matrix is introduced to quantify the clutter nonstationarity, and in this way we realize adaptive segmentation of the clutter range-Doppler spectrum. Then the different clutter suppression methods including pulse Doppler (PD) processing, two-dimensional space-time adaptive processing (2D STAP), and three-dimensional STAP (3D STAP) are used to suppress clutter in different regions. The theoretical analysis and simulation results demonstrate that compared with 3D STAP, the proposed method not only guarantees clutter suppression performance, but also reduces the computational complexity to an extent.
Positioning of any Subscriber in Land-Atmospheric Communication Links by Flying Vehicle Antenna

In this work, we examine the positioning of any subscriber located in built-up areas with a dense buildings layout and their overlay profile. The positioning is evaluated for different elevations of flying platforms (e.g., drones, quadcopters, aircrafts etc.) antennas and stationary or mobile subscriber (SS or MS, respectively) antenna, located below and above the rooftops. The positioning is based on the stochastic multi-parametric approach that successfully was adapted for prediction of radio signals in the space, AOA, TOA and Doppler frequency spread domains in land-to-land communication links. We use the signal power variations and its distribution in angle-of-arrival (AOA), time-of-arrival (TOA), or time-delay (TD) and Doppler shift spread (DS) domains. On the framework of the stochastic approach, the effects of TD spread and DS on signal distortion for several moving aircrafts and several ground-located subscribers have been analyzed via some experiments carried above an airport. Comparison between the predicted and experimentally observed data gives a good coexistence of the proposed stochastic approach compared with those proposed by other researchers and finally makes possible to localize each subscriber located in dense urban environment based on knowledge of its signal power distribution in the AOA, TD and DS domains.
Input-delay satellite optimal tracking control based on differential games

Satellite pursuit-evasion game has been studied for many years as a research direction of pursuit-evasion game. In the actual scenario, the input delay is inevitable, including the measurement delay, transmission delay, controller’s delay and so on. In this paper, the pursuit-evasion game of satellite with input delay is studied. Firstly, the game with input delay is modeled. Then the necessary conditions for saddle point solution of game are obtained by applying the theory of calculus of variations. Finally, the optimal feedback controls of both pursuer and evader are obtained. Furthermore, we studied the relationship between initial state, interception time, control boundary value and input delay when both players adopted the optimal control solution, and obtained an equation describing this relationship, which was analyzed through simulation. The research results of this paper provide a reference for the further study of the satellite pursuit-evasion game with input delay.
Power Allocation for Multipair Massive MIMO FD Relay Systems with Low Resolution ADCs

To reduce energy consumption, relay stations with massive antenna arrays can be equipped with low resolution analog-to-digital converters (ADCs). Due to powerful loop interference in full-duplex (FD) relaying, however, low resolution ADCs generate strong quantization noise that has severe impact on the throughput of the system. In this paper, the throughput and energy efficiency of a FD decode-and-forward relay system with low resolution ADCs is investigated. Based on the mathematical analysis, a novel iterative power allocation scheme that mitigates the impact of the quantization noise via reducing the received LI power is proposed. The power allocation scheme aims at maximizing the end-to-end achievable rate by adjusting the per-link transmit powers at the relay. The numerical results show that compared to half-duplex relaying, using FD with the proposed power allocation scheme improves the system throughput and energy efficiency significantly.
Advanced Modeling of an isotropic Three-Axis magnetic field probe using coils and a near field source approach

The main topic of this paper is the modeling of a magnetic field probe by using coils and a near field source approach in CST MWS. We first explain an older field probe model where we used discrete field probes to model the coils of the magnetic probe. Then the basic theory of the field probe model will be explained. By doing this we will show the reason or motivation for switching from the old field probe model to the new model which uses coils. Furthermore the model will be validated in an inhomogeneous magnetic field created by a simple wire structure for different probe orientations by using simulations and measurements. After the validation we will use a near field source approach to split the model into two parts. Finally we will show how we can use CST MWS to find the probe orientation which leads to the biggest value of B.
New Fault Tolerance Method for Open-Phase PMSM

Once the motor stator winding is opened, balanced three-phase windings turn into unbalanced two-phases windings. Unfortunately, by conducting Clarke and Park transformation for open-phase PMSM, complete decoupling of the torque and flux cannot achieve. To maintain the rated torque, the two remained phase currents have to be modified as sinusoidal currents with 60° phase difference (not 120°). As a result, the current controller design becomes complicated. In order to solve this problem, a new fault tolerance method for the open-phase PMSM is proposed in this paper. It is designed based on a novel reference frame transformation. Through proposed frame transformation, the modified sinusoidal time-varying current commands are turned into dc variables in the redefined synchronous rotating frame. Hence, the design of the open-phase PMSM current controller can be simplified. This method can deal with different phase open fault and different current control mode ( $i_{d}=0$ or $i_{d}\ne 0$ mode). In addition, considering that the neutral current ripple at usual switching frequencies may be very high, an optimal additional inductance that inserted into the neutral wire is designed. With the designed additional inductance, complete decoupling can be achieved. Experimental results confirm that the reliability and the performance of the PMSM drive can be improved distinctly with the proposed open-phase fault tolerance strategy.
Average Linearization of Phased Array Transmitters Under Random Amplitude and Phase Variations

We study the impact of amplitude and phase differences between the parallel power amplifier (PA) branches in a phased array and their impact on the performance of the digital predistortion (DPD). The DPD coefficients are estimated from the array response in the far-field. The DPD coefficients need to be updated for changes in the nonlinear behavior of the PAs due to amplitude and phase variations. We present a training mechanism which makes the DPD robust to branch specific amplitude and phase weights and can tolerate these variations without the need of adapting to individual changes in the nonlinear behavior of the PA branches. The DPD is trained for a set of random amplitude and phase weights following normal distribution and the resultant mean DPD coefficients are used for predistortion. The simulation results show that the mean DPD can achieve the same average linearity performance as the continuously trained reference DPD for 32 elements uniform array.
H−/L∞ Fault Detection Observer Design for Uncertain Takagi-Sugeno Fuzzy Systems

This paper presents a fault detection observer design method for Takagi-Sugeno fuzzy systems in the presence of bounded parametric uncertainty, process disturbance and measurement noise. To make the proposed observer is sensitive to faults and robust to uncertainties, H−/L∞ performance index are applied to characterize the performance of fault sensitivity and disturbance robustness, respectively. Due to the advantages of L∞ analysis, an intuitive dynamic threshold is also synthesized. Sufficient design conditions are derived and expressed in terms of linear matrix inequalities. Numerical simulations verify the effectiveness of the proposed method.
Performance of Massive-MIMO OFDM system with M-QAM Modulation based on LS Channel Estimation

A Least Squares Channel Estimation (LSCE) method is designated for a Massive MIMO system combined with Orthogonal Frequency Division Multiplexing (OFDM) and higher order modulation technique. The performance of ZF and MMSE detectors is evaluated with $(Nt\times Nr)$, (50 × 100) and (50 × 300) respectively antennas array, for various modulations techniques 16-QAM, 64-QAM, and 128-QAM, and for various OFDM sub-carriers 64, 256, 512 and 1024. The performance is determined in terms of Bit Error Rate (BER). Increasing the number of Bits/symbol provides an increase of BER both the ZF and MMSE detectors for an antennas array 50 × 100; Whereas increasing the number of OFDM sub-carriers provides a decreasing of BER. Combining 128-QAM modulation with 1024-sub-carriers and increase the receiver antennas array three times (i.e., 50 × 300), decreases more the BER both the ZF and MMSE detectors. Consequently the ZF and MMSE detectors provide a best BER so a best system performance.
GSM controlled location specific garbage collecting Smart-bin

Garbage collection and dumping of garbage is tedious job. Though lots of garbage bins are installed in various places nowadays but very few people use it. Moreover, when garbage becomes full it takes days to dump it and hence it overflows. In this paper, we propose a smart-bin which can collect garbage from the user by sensing the vicinity of the user by opening the lid or move to the location of the user when it is called using message. This design not collects garbage but also notifies when the smart-bin is full and also moves the dumping zone to dump the collected garbage. This paper considers a office room scenario and proposes the methodology accordingly.
Mitigation techniques for phase noise degradation under vibration of microwave sources for airborne radar applications

Ultra low phase noise is required for microwave sources of airborne radars to detect very weak target echo signals. Particularly in pulse doppler radars, at close-in relative velocity, an ultra low phase noise of Exciter source is an essential requirement. But airborne radars undergo severe vibrations during its operation. The phase noise degrades under vibration which is undesired. Different mitigation techniques are used to combat the phase noise degradation during vibration. In this paper, the mitigation techniques used for phase noise degradation under vibration of microwave sources for airborne radar applications is presented.
Automatic Classification of Clinical Skin Disease Images with Additional High-Level Position Information

Since skin disease is one of the most common human diseases, intelligent systems for classification of skin diseases have become a new line of research in deep learning, which is of great significance for both doctors and patients. Some skin-disease datasets have already been published, such as the SD-198 dataset, which contains 6584 clinical skin-disease images of 198 categories. However, because of the diversity of clinical dermatology, previous works have showed that the performance of deep visual features is not as good as or even worse than hand-crafted features for skin disease classification. In this paper, we propose an SD-198-P dataset, which includes additional high-level position information in the SD-198 dataset to guide the generation of better deep visual features. Our experiment shows that, after adding the position information, the performance of deep visual features is better than that of hand-crafted features. To the best of our knowledge, our method outperforms the current state-of-the-art clinical skin disease classification methods.
Unscented Kalman Filter Based Beam Tracking for UAV-enabled Millimeter Wave Massive MIMO Systems

Fueled by the incessantly growing need of high-speed wireless access in the fifth generation (5G) wireless networks, unmanned aerial vehicle (UAV)-enabled millimeter wave (mmW) massive multiple input multiple output (mMIMO) has become an attractive solution to provide high capacity wireless transmission. In this paper, we proposed an unscented Kalman filter (UKF) based beam tracking scheme for the UAV-enabled mmW mMIMO systems to manage the challenge of rapid channel variation due to UAV’s movement. Considering the nonlinearity of beamforming and the performance advantage of UKF to a broader nonlinear estimation problem, we utilize UKF to estimate the channel angle vector to obtain the channel state information which is indispensable for beamforming. Numerical results showed that the proposed beam tracking scheme was capable of improving the accuracy of the estimated beam angle.
Features of real and imaginary motor activity on EEG and fNIRS signals for neurorehabilitation

Experimental design for recording of EEG and fNIRS during performance of real and imaginary movement was proposed. Set of experiments was conducted in accordance with this design and obtained EEG and fNIRS dataset was analyzed. Analysis allowed to introduce certain features in time- frequency domain that can be used to separate real motor activity from imaginary.
A Novel BIST Algorithm for Low-Voltage SRAM

A novel Built-In Self-Test (BIST) algorithm is proposed in this paper, which is used for testing low-voltage SRAM. The algorithm is the improvement of March C+ algorithm, which integrates the continuous write 0 and write 1 operations to cover more fault models like Read Destructive Coupling Fault (CFrd), Write Destructive Coupling Fault (CFwd) and Write Disturb Fault. Consequently, higher fault coverage is achieved than traditional March algorithm. The proposed algorithm is implemented by user defined algorithm (UDA) of Mentor tools. In order to verify the effectiveness of the algorithm, a low-voltage 8T SRAM chip is designed and tested based on SMIC 40nm LL CMOS process. Simulation results show that the proposed BIST algorithm named March-LV for low-voltage SRAM arrays covers 100% target faults. The test results further verify the feasibility of the algorithm.
Hybrid Energy Ratio Allocation Algorithm in a Multi-Base-Station Collaboration System

Network densification in the 5G system causes a sharp increase in system energy consumption, a development which not only increases operating cost but also carbon emission. With the development of smart power grids, multi-source energy supply and time-of-use (TOU) power price have become effective methods for reducing system energy price (namely, total energy cost). However, the energy generation velocity of renewable energy (RE) is significantly influenced by weather factors. Thus, their energy generation entails large fluctuations, and the system energy allocation strategy involves enormous challenges. Therefore, the energy generation velocity of wind power was combined in a multi-base-station (multi-BS) collaboration system. A multi-BS collaborative energy allocation algorithm called hybrid energy ratio allocation (HERA) algorithm was proposed under RE generation uncertainty. This algorithm can balance the TOU power price of a smart power grid and the energy storage of a BS. The energy supply modes of different energy ratios were adopted following different power prices and energies stored by different BSs, which can effectively mitigate the effect of RE generation fluctuation on the energy allocation strategy of the system. Simulation results indicated that the HERA algorithm can complete the energy ratio allocation of the system and reach the lowest energy cost of BSs.
Prediction of Melanoma from Dermoscopic Images Using Deep Learning-Based Artificial Intelligence Techniques

Recently, hospitals and health care institutions have increasingly been addressing clinical decision support systems (CDSS), which can offer specific patient assessments or recommendations to physicians and health care professionals. It is very useful to develop CDSS which can help physicians to make meaningful and correct decisions by using existing data or image sets. Also, CDSS increases the diagnostic accuracy of diseases, provides significant facilities in precision medicine applications, increases operating efficiency of hospitals and reduces costs. In this context, the proposed project intends to create a model using pre-trained networks (i.e. VGG-16,) based on deep learning (DL) that can successfully predict the melanoma using dermoscopic images. The current study provides clinical support to physicians in the medical decision-making process for the diagnosis of melanoma.
The emperical mode decomposition for ECG signal preprocessing

Preprocessing of ECG signals allows increasing efficiency and effectiveness of diagnosis of socially significant heart diseases. Development of algorithms and programs for digital processing of ECG signals should ensure preservation of informatively significant components, adaptability to individual features and parameters of a patient, as well as the possibility of using online daily monitoring in mobile systems during human free movement activity. To solve the indicated problems, it is proposed to use the Hilbert-Huang transform, which will allow ensuring the reduction of the level of the most characteristic interference with minimal distortion of the useful component of the ECG signal.
A New Output Feedback Controller Synthesis for T-S Fuzzy Affine Impulsive Systems

This paper designs a robust output feedback controller for a class of T-S fuzzy affine impulsive systems. Through a descriptor-system-based scheme and based on piecewise quadratic Lyapunov functions, sufficient conditions for exponential stability of the resulting closed-loop system are obtained, and the controller gains can be acquired by solving a set of linear matrix inequalities. A simulation example is given to show the validity of the proposed approach.