Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasi-Free-Standing Bilayer Graphene Hall-Effect Sensor

This paper presents recent progress in the development of the new type of Hall-effect sensor with the graphene layer acting as sensing material. Newly developed Hall-effect sensor is made of quasi-free-standing bilayer graphene structure. The graphene structure is placed inside the standard QFN-32 package for integrated circuits and mounted on the miniature printed circuit board with four leads, which allowed obtaining operational magnetic field sensor. Basic functional properties of the developed sensor were investigated as well as time drift and temperature dependence of these parameters. Performed investigation indicates high linearity of the sensor within the tested range of an external magnetic field. The measured current-related sensitivity of the sensor is about 50 V/AT, without any amplification of the output signal from the graphene structure. Also, long-term tests of sensitivity and offset voltage were performed. The results indicate high stability of the investigated parameters within long time. Obtained results are very promising and indicate the possibility of utilization of the developed sensor in measurement application.
A Two-Stage Optimization Method of Dynamic VAR Compensation for Multi-infeed HVDC-AC Receiving System

The influence of High Voltage Direct Current (HVDC) on AC-DC system is not considered in most optimization problem of dynamic var compensation. A two-stage optimization method of dynamic var compensation for multi-infeed HVDC-AC receiving system is proposed in this paper. The first stage is partition. In partition stage, a sensitivity-controlled partition method for AC-DC system var compensation is proposed considering the influence of the multi-infeed HVDC system. The second stage is optimization. In optimization stage, for each area, there are three steps: 1) the trajectory violation integral index considering HVDC commutation failure is analyzed; 2) the optimization objective considering both the voltage trajectory sensitivity integral and economic cost factors is established; and 3) the imperialistic competitive algorithm (ICA) is improved and utilized to solve the optimization problems of the dynamic VAR compensation allocation for each sub-area. Finally, the applicability and effectiveness of the proposed method are illustrated using the Guangdong power system as test system.
Real-Time Magnetomotive Ultrasound Imaging Using a Recursive Estimator

In magnetomotive ultrasound, real-time capability is useful for the alignment of magnetic coils and ultrasonic transducers and it is considered as necessary for clinical applications. Since usually a harmonic motion is induced, the magnitude of the displacement is often estimated via Fourier transform of the phase of IQ-data. However, a standard block-by-block processing of IQ-data data can limit estimator performance. Here, hardware-and estimator-parameters are decoupled by implementing a recursive algorithm. Therefore, a sliding windowed infinite Fourier transform was used. The different characteristics of the block-by-block and the sliding processing are demonstrated. A higher performance of the recursive algorithm was shown with simulated data at low signal-to-noise ratios. The implementation on an ultrasound research platform confirmed real-time capability. The presented algorithm is expected to be easily implemented on any ultrasound system. Future work needs to address the optimal parametrization in real-world scenarios.
Evaluation of technological development for the definition of Industries 4.0

The impact on the industry generated by the new ICTs stimulates the detection of failures, the improvement of processes, and the acceleration in production times, all variables that significantly alter the levels of productivity in the different industrial sectors where they are implemented.Although nowadays companies use different types of technologies in their administration, marketing or finance processes, among other areas, the insertion of technologies in the automation and control of production processes in the manufacturing industry is generating a new industrial revolution that changes the paradigm of process control by collecting probabilistic samples to work with the totality of the data, managing to store and analyze large volumes of data in real time, impacting on the optimization of production processes.Technologies that permit data capture by sensors with the insertion of IoT, the processing of large volumes of data with Big Data techniques, as well as the integration of complete production information with areas of purchase, sale or logistics allows a comprehensive management of the companies in what is known as the industry 4.0. The present article introduces a taxonomy that identifies which ICTs are specifically implemented in the industry at present, according to the functions that they fulfill in the organizational structure and which are the technologies that are being developed that directly impact on productivity levels and would allow to transform a company into an industry 4.0.
Resource Allocation and User Grouping for Sum Rate and Fairness Optimization in NOMA and IoT

In this paper, we present the joint optimization of sum rate and fairness for contention based uplink multiple access with non-orthogonal multiple access (NOMA) communication system by resource allocation and user grouping. In particular, we study the cases of many users sharing the same resources that address application of the the internet of things (IoT). The key feature of contention based multiple access is to serve multiple users at the same time and frequency. With different power levels and user grouping, it can achieve better spectral efficiency over conventional orthogonal multiple access (OMA). However, unlike the OMA system, NOMA results in additional inter-user interference (IUI). It has also been shown that, without proper resource allocation for users in the uplink NOMA, the weak users can always be in outage. In this work, we have developed algorithms on subbands assignment, user grouping, and power allocation for joint optimization of sum rate and fairness. The algorithm allocates resources iteratively to handle the IUI in each iteration. Given a number of $N_{s}$ subbands allocation to each user, we could prevent starvation of poor users, e.g. cell edge users. We have also compare and analyze the sum rate and fairness performance with different combination of $L$ and $N_{s}$. We also find that, by properly limiting the maximum number of subbands each user can use, the system could better exploit multi-user diversity to improve the sum rate and hence the energy efficiency. The numerical simulations are also conducted to verify the results.
Decentralized Reinforcement Learning Based MAC Optimization

In this paper, we propose a novel decentralized framework for optimizing the transmission strategy of Irregular Repetition Slotted ALOHA (IRSA) protocol in sensor networks. The proposed method is inspired by reinforcement learning algorithms. To deal with sensor nodes limited lifetime and communication range, we allow sensor nodes to decide how many packet replicas to transmit and when to transmit them considering only their own buffer state. We show that this information is sufficient and can help avoiding packets' collisions and improving the throughput significantly. We solve the problem using the decentralized partially observable Markov Decision Process (POMDP) framework, where we allow each node to decide independently of the others how many packet replicas to transmit and when. The performance of the proposed method is compared with the native IRSA protocol. The results make clear that our method leads to large throughput gains without imposing nodes coordination, in particular, when network traffic is heavy.
Wideband Quad-Polarization Reconfigurable Antenna Using Switchable Feed Network With Stable Unidirectional Radiation Patterns

A wideband antenna with quad-polarization reconfigurable operation is proposed. It consists of a crossed tri-port feed linearly polarized (LP) radiation structure and a switchable feed network. A dual-port LP element with wideband characteristic and good port isolation is first introduced. The inherent 180° phase difference (PD) between two feed ports vastly simplifies the complexity of the design and improves the efficiency of the feed network. By assembling two grooved LP elements, the crossed tri-port feed radiation structure is formed. Meanwhile, a wideband 90° phase shifter and an unequal Wilkinson power divider are applied in designing the switchable feed network. Through controlling the PIN diodes implanted in the feed line, four working modes with equal amplitude and reconfigurable PD (0° or 90°) are achieved. Based on this, the desired quad-polarization reconfiguration, including two orthogonal linear polarization (LP) states and two orthogonal circular polarization (CP) states, is realized. Measured results show that the proposed antenna has a wide overlapped operation impedance bandwidth of 84.5%, ranging from 1.36 ~ 3.35 GHz, for all polarization states within a compact volume. The measured 3-dB AR bandwidth is 100%, covering the operation impedance frequency range. Radiation patterns exhibit unidirectional and stable quality from the point view of beam slant and split. Besides, a peak efficiency of 75% and a peak gain of 7.8 dBi are achieved.
Visible Watermarking Assessment Metrics Based on Just Noticeable Distortion

During the last two decades, several visible watermarking algorithms with different characteristics have been proposed. In the context of visible digital watermarking, watermark visibility and its non-obtrusiveness are essential issues. However, assessment metrics were not established to evaluate these two issues until now. Due to the lack of established assessment metrics, visibility is evaluated in a subjective manner and the obtrusiveness caused by the visible watermark is evaluated by well-known image quality metrics, such as peak signal noise ratio, comparing the host image and the watermarked image. In this paper, we propose four assessment metrics for visible watermarking algorithms, in which the global visibility, global obtrusiveness, local obtrusiveness in host edge region, and a global quality of watermarked image are evaluated using a couple of criteria based on the human visual system and a pixel-based just-noticeable distortion function. The evaluation results show the reliability of the proposed metrics used for measuring the above-mentioned aspects when several visible watermarking algorithms are employed as well as in several scenarios where an attempt to remove the watermark pattern from the watermarked content was made.
Ultrasonic Array Imaging Through Reverberating Layers for Industrial Process Analysis

In this paper, ultrasonic phased arrays are investigated as an imaging tool for industrial process analysis. Noninvasive process measurement, via transmission of information through a vessel wall, typically requires a window to create an optical path between the sensor and the process. Ultrasonic array imaging provides a means to overcome this barrier as it is specifically used to image into optically opaque structures. However, the large acoustic impedance mismatch between the steel process vessel and water load results in reverberations clouding the image scene containing reflections from within the process fluid. A methodology to identify and remove this reverberation interference from the image scene is proposed using subspace analysis coupled with phase coherence imaging. A 32 element, 5 MHz finite element array model mounted to the outside of a steel vessel wall is used to demonstrate the application of this methodology to a typical industrial process environment. The final image is free of reverberation artefacts, providing a means to accurately extract quantitative information about the process from these images.
Information-Centric Networking With Edge Computing for IoT: Research Challenges and Future Directions

Cloud computing is a paradigm that offers storage, computation, and software services on demand in the Cloud and far from end users. These services and computations are then extended to the Edge of the network with the Edge computing paradigm. This paradigm offers computation, data, and application services in close proximity of end users. Future Internet architectures will result in a fast information response time, and low latency will be a main feature of evolving 5th generation (5G) radio networks. To ensure the widespread adoption of 5G applications, low latency, security, mobility, and scalable content distribution support is required. Information-centric networking (ICN) is a newly proposed future Internet paradigm in which communication is based on content names irrespective of their locations. At the same time, ICN promises efficient content delivery, mobility support, scalability, and security for content. The Edge computing and ICN provide an opportunity to reduce latency, support mobility, security, and scalability. In this paper, discussions on Edge computing with ICN are provided. In detail, the Edge computing proposals, use cases, differences among Edge computing proposals and drivers for Edge computing are investigated. The Edge computing standardization, research, and industry/vendors collaboration overview are studied. Applications of ICN integration in Edge computing and their advantages and limitations are highlighted. We conclude our paper by describing potential directions for future research in the field of ICN over Edge computing.
Dynamic In-Band Self-Backhauling with Interference Cancellation

Millimeter wave frequency bands are expected to be used in future 5G networks to provide high data rates. However, higher frequency bands are characterized with high path loss attenuation and sensitivity to the blockages. Thus, deployment of dense networks using smaller cell sizes is important for coverage. To this end, in-band wireless relaying can be used to realize smaller cell sizes at reduced backhaul cost. Wireless relaying may however sacrifice capacity because of half-duplex constraint. In this paper, we investigate the performance of the millimeter wave (mmWave) network employing self-backhaul relay nodes and centralized transmission coordination. In particular, we look at the usage of interference cancellation (IC) receivers to enable backhaul uplink multiple access channel (BU-MAC) and full-duplex (FD) access/backhaul transmission which can boost capacity of relaying. Through simulation results we show, that the 5th% E2E throughput is improved by around 90% with BU-MAC scheme and over 120% in FD scheme. The average throughput of relay downlink increases by around 50% and 80% with BU-MAC and FD schemes resnectively.
Determination of the optimal size of the Energy Storage Systems in photovoltaic systems connected to the electricity network, through Mixed-Integer Linear Programming

This research and development project presents the optimization of BESS energy storage systems (Battery Energy Storage System) in photovoltaic systems connected to the electricity grid, using mixed integer linear programming algorithms. In the development of this work, the creation of a database of both load and generation profiles was first performed, the data obtained served to propose the mathematical optimization model together with their respective restrictions. The optimization model was solved through mixed linear programming algorithms and finally the optimization algorithm developed was tested for the electrical system of Santa Cruz Island, Galápagos province, where the integration of the electric grid with renewable energies is carried out as the PV generation.
Adaptive Updating of Power System Transient Stability Prediction Model Based on Data Inheritance

Transient stability prediction using machine learning algorithms has been highly concerned. Existing researches have made great progress in simplified scenarios, while these simplifications cannot be made in a practical problem. Considering two typical characteristics of power systems: time-varying and data-increasing, two methods of prediction model updating are proposed in this paper. Transfer learning is applied to adding samples to a time-varying system with insufficient training samples. Incremental learning is used to update the prediction model with constantly increasing training samples. Both two methods make the prediction model more robust in a dynamic power system. In IEEE 39-bus system, the proposed methods are tested in angle and frequency stability problem respectively. Results show that transfer learning makes more accurate prediction in a time-varying power system than traditional method and incremental learning enables model updating to be fast enough to be applied online. More importantly, the proposed updating methods can be further investigated for practical application.
Toward the LPV Approach for Adaptive Distributed Consensus of Lipschitz Multi-Agents

This brief investigates an adaptive distributed consensus control of Lipschitz nonlinear multi-agent systems by means of output feedback. The Lipschitz nonlinearity is reformulated via a linear parameter-varying approach. Compared with the recent existing results, our consensus protocol is less conservative and feasible for larger values of Lipschitz constant. Furthermore, the results of the proposed protocol are extended to the case of switching topologies. Necessary and sufficient conditions are derived for the proposed design conditions to compute the parameters of consensus control strategies. Finally, numerical simulations are included to demonstrate the effectiveness of the proposed consensus protocol.
Robust Control of Grid-Connected Photovoltaic Systems Under Unbalanced Faults Without PLL

This paper presents the design of a robust control scheme for grid-connected photovoltaic systems subjected to severe operating conditions such as grid faults, abrupt set-point changes, parametric uncertainties, and unknown disturbances. During unbalanced faults, the scheme is able to deliver either constant real and reactive powers, or constant real power with sinusoidal currents to the grid, without requiring a phase-locked loop or symmetrical component decomposition. Moreover, the same controllers are used under normal operation and grid faults. These feats result in a control system having lesser computational requirements and complexity, and a lower number of design steps in comparison to some existing schemes. The dc-link voltage controller is based on active disturbance rejection control, and phase currents are controlled using repetitive control based on the internal model principle. The controllers are designed using linear matrix inequality constraints that can be solved by readily available tools. A number of simulation test cases are presented using the SimPowerSystems toolbox of the MATLAB/Simulink computing environment to demonstrate the performance of the control scheme under various types of grid faults, parametric uncertainties, and abrupt changes under operating conditions. Controller performance is also validated through digital implementation on a low-cost microcontroller.
Mitigation of Phase Cancellation for Efficient Decoding and RF Energy Harvesting in Tag-to-Tag Communications

In this paper, we investigate a phase cancellation problem that occurs in tag-to-tag communication systems. These are the systems wherein several tags communicate with each other by backscattering an external constant wave signal. A transmitting tag modulates baseband information into the reflected signal using backscatter modulation. At the receiving tag, the received signal is the superposition of the transmitted signals by the carrier emitter and the transmitting tag. The resulting signal is demodulated using the envelope detector that causes the phase cancellation problem. This problem exists in all tag-to-tag communication systems and largely impacts on the reliability and communication range. We theoretically analyze and experimentally demonstrate this problem for the tags that use amplitude-shift keying-based backscattering. We propose an algorithm for the mitigation of the phase cancellation based on the phase rotation control. We examine the performance of the proposed algorithm through theoretical analysis and computer simulations. We confirm that the proposed algorithm can significantly mitigate the phase cancellation.
Provisioning QoS in Wi-Fi Systems With Asymmetric Full-Duplex Communications

The traffic volume carried by wireless local area networks (WLANs) continues to increase at a rapid pace. Full-duplex communication is a key solution for satisfying the growing traffic demand, enhancing spectrum efficiency, and reducing latency for WLAN users. In this paper, we consider the application of asymmetric full-duplex (AFD) communications in WLANs, exemplified by a Wi-Fi system. Our system model relies on a full-duplex-enabled Wi-Fi access point to simultaneously transmit uplink and downlink to a pair of half-duplex Wi-Fi stations. Providing QoS guarantees in WLANs with AFD communication capabilities is challenging due to inter-node as well as residual self-interference. The heterogeneity of the QoS requirements between paired uplink and downlink stations further complicates the problem. To tackle these challenges, we introduce a framework called AFD-QoS, which incorporates AFD communications in WLANs and supports QoS. AFD-QoS consists of three components: 1) AFD-enabled uplink/downlink station-pair selection algorithm; 2) AFD-enabled block-acknowledgment session initiation/termination protocol; and 3) joint transmission rate/AFD communication mode adaptation scheme. Our adaptation scheme relies on intelligent and cognitive approaches to improve Wi-Fi networks awareness about channel dynamics as well as inter-node and self-interference. We introduce new intelligent MAC-layer procedures for supporting QoS services in AFD communications, and cast light on many challenges and their solutions. Our simulation results indicate that AFD-QoS outperforms classical half-duplex frameworks and achieves up to 90% of the optimal AFD performance.
A Method of Robot Base Frame Calibration by Using Dual Quaternion Algebra

When the robot is required to execute a certain task in the world coordinate system (WCS), it is necessary to find the coordinate transformation between the robot base coordinate system (RBCS) and WCS to enable the high precision motion planning. This paper presents a simple and accurate method that allows a simultaneous computation of the coordinate transformations (i.e., rotation and translation) from WCS to RBCS. Based on the dual quaternion, the robot kinematic model and formulas for calculating the transformation are derived, which allow calculating the rotation and translation simultaneously. Taking the unit dual quaternion as a constraint, the Lagrangian multiplier method is employed to obtain the optimum transformation. Both simulation and experiment results show that higher calibration precision is obtained. The proposed approach has certain reference value and guiding sense for other calibration problems.
Reducing the Severity of Black Hole and DDoS Attacks in MANETs by Modifying AODV Protocol using MAC Authentication and Symmetric Encryption

In Mobile Ad hoc Networks (MANET), Ad hoc On Demand Distance Vector (AODV) protocol suffers from the black hole attacks that disrupts the functionality of the network by displaying false information during routing process. Such an attack eventually affects the network performance. In this paper, an authentication mechanism based on lightweight encryption algorithm and Media Access Control (MAC) authentication are proposed for black hole and Distributed Denial of Service (DDoS) attacks. The proposed technique is validated in Network Simulator 2 (NS2) whose results show that the approach offers prompt route verification with high throughput and generating less routing overhead avoiding black hole attacks.