Miracle-dz/UniFAR-FTUs huggingface dataset:
User Satisfaction-Aware Resource Allocation for 5G Green Vehicle Platooning

Platooning has been identified as a green vehicular traffic management strategy to improve energy efficiency, environmental sustainability, road capacity, and on-road safety, in which the platoon' stability is ensured through the exchange of control information among vehicles. Proximity-based device-to-device (D2D) communications with high transmission rate, is more suitable for delay-sensitive inter-vehicle communications. In order to achieve the above benefits, there are several fundamental challenges that should be addressed: 1) how to design an appropriate communication mode combined with D2D technology, to face complicated road conditions; and 2) how to efficiently allocate the limited resources to guarantee each users' service satisfaction. In this paper, a predecessor-following communication mode-based on D2D is proposed to guarantee high-rate transmission of control information. After that, a time division-based intra-platoon resource allocation mechanisms is briefly introduced, which aims at allocating resources for D2D users (DUEs) efficiently within the platoon. Then, based on the social relationships among users, an optimization problem about resource allocation inter platoon is formulated and solved with the objective to maximize the user satisfaction. Numerical results show that the proposed mechanism can not only improve user satisfaction, but also guarantee the minimum transmission rate of each platoon.
A Dynamic Event-Triggered Rule for a Control System

In this article, we summarize several types of event-triggered methods that have been proposed in the literature, and propose a new class of dynamic event-triggered method for a control system. Stability of the system after adding new dynamic variables is proved, and the influence of parameter selection on system control performance is discussed. In addition, the average sampling interval and performance of different event-triggered methods are considered in the case of ensuring the same Lyapunov function decay rate. We discuss the event-triggered rules both in linear and nonlinear control system. Finally, we provide some simulation examples to verify the theoretical results.
On Improving Face Generation for Privacy Preservation

Replacing faces in image and video content with generated ones (e.g., using generative adversarial networks, GANs) has gained attention recently, as it enables resolving privacy issues in image and video data used for visualization purposes or training data in multimedia analysis and retrieval systems. Privacy issues should be addressed when visual content enters the system, as identifying and removing content later (which may be necessary due to the shifts in legislation and users' increased awareness) is a tedious and costly task. This paper proposes two improvements of face generation: First, we propose the use of portrait segmentation on the training data of the GAN, in order to generate images that are not only cropped to the face region, which may cause artifacts during insertion. Second, we add a face detection term to the loss function, in order to better guide the training process. The results show that these modifications enable creating uncropped face images achieving the same or better performance than for closely cropped images. We use the detectability of the generated faces as an evaluation metric, discuss the limitations of such a metric and propose enhancements for better comparability. We also demonstrate that the aim of anonymization is achieved by running face recognition on the modified images from the LFW data set.
Path Integral Policy Improvement with Differential Dynamic Programming

Path Integral Policy Improvement with Covariance Matrix Adaptation (PI2-CMA) is a step-based model-free reinforcement learning approach that combines statistical estimation techniques with fundamental results from Stochastic Optimal Control. Basically, a policy distribution is improved iteratively using reward weighted averaging of the corresponding rollouts. It was assumed that PI2-CMA somehow exploited gradient information that was contained by the reward weighted statistics. To our knowledge we are the first to expose the principle of this gradient extraction rigorously. Our findings reveal that PI2-CMA essentially obtains gradient information similar to the forward and backward passes in the Differential Dynamic Programming (DDP) method. It is then straightforward to extend the analogy with DDP by introducing a feedback term in the policy update. This suggests a novel algorithm which we coin Path Integral Policy Improvement with Differential Dynamic Programming (PI2-DDP). The resulting algorithm is similar to the previously proposed Sampled Differential Dynamic Programming (SaDDP) but we derive the method independently as a generalization of the framework of PI2-CMA. Our derivations suggest to implement some small variations to SaDDP so to increase performance. We validated our claims on a robot trajectory learning task.
Miniaturised Distributed Transceivers for Far-Field Microwave Imaging

A novel microwave imaging system using miniaturised distributed sensors (transceivers),operating in far-field is proposed. The system is quick to deploy and is based on scattered miniature transceivers scanning a region which makes it a surreptitious one. Each sensor will consist of a transmitting and a receiving antenna, microwave signal generation and reception circuitry. The information from all the individual sensors will be conveyed wirelessly to a remote controller where using a simplified Synthetic Aperture Radar processing, a useful image is developed from the information collated. The most important feature of the proposed system is that a person being scanned, will not be aware of it. In the present work, the proposed transceiver circuitry (easily implementable using CMOS-RF) is replaced with a network analyzer for laboratory testing. Target detection and shape estimation of irregular shaped targets is achieved for exposed and partially shielded targets.
Microgrid with Five-Level Diode Clamped Inverter Based Hybrid Generation System

This paper presents a new architecture for standalone microgrids incorporating 5-level diode clamped inverter (DCI) for interfacing hybrid generation system (HGS) with ac loads. The HGS involves photovoltaic (PV) arrays, battery packs and diesel generator (DG) that act as individual dc sources for DCI which in turn eliminates the need of input capacitors for DCI and the associated voltage balancing. For the DCI output voltage level of 2Vdc, the DG and the combination of PV array and/or battery supply the load. Similarly, the PV array and/or battery feed the load when the voltage level is Vdc. Likewise, for the levels -Vdc and -2Vdc, a separate PV array, battery pack and DG are operated in similar manner. In case the PV power is greater than the demand, it can be used to charge the battery. Conversely, if the PV power cannot meet the demand, then the charged battery pack can assist to feed the load. The system reliability and selfsustenance capability is enhanced with the use of DG. Additionally, as no synchronization with the DG is required, the need of fundamental current extraction is eliminated. The operation of the proposed standalone microgrids with 5-level DCI based HGS is analysed for different operating conditions.
Manufacturing of Sub-20 NM Wide Single Nanowire Devices using Conventional Stepper Lithography

Single nanowires have a broad range of applications in chemical and bio-sensing, photonics, and material science, but realizing individual nanowire devices in a scalable manner remains extremely challenging. This work presents a scalable and flexible method to realize single gold nanowire devices. We use conventional optical stepper lithography to generate notched beam structures, and crack lithography to obtain sub-20-nm-wide nanogaps at the notches, thereby obtaining a suitable shadow mask to define a single nanowire device. Then a gold evaporation step through the shadow mask forms the individual gold nanowires with positional and dimensional accuracy and with electrical contacts to probing pads.
Interference Measurement Methods in 5G NR: Principles and Performance

In this paper, we describe and analyze two different interference measurement (IM) methods for 5G New Radio (NR) networks, with particular emphasis on cell-edge users and thus interference limited scenarios. Specifically, the non-zero-power channel state information (CSI) reference signal (NZP CSI-RS) based method and the CSI interference measurement (CSI-IM) method are defined, studied and compared. In general, IMs are an essential ingredient in efficient link adaptation techniques, particularly in the form of channel quality indicator calculations which build on interference and channel response measurements at user equipment. Hence, we specifically study the impact of different IM methods on 5G NR link adaptation and throughput performance, incorporating both inner loop and outer loop link adaptation procedures. Different IM methods are defined, discussed and their basic operation principles are revised, including system overhead aspects, and detailed performance evaluation and comparison is provided. Our results show that NZP CSI-RS method allows to achieve more accurate IM than CSI-IM method, when measuring non-precoded interference and noting practical channel estimation and measurement errors. The CSI-IM method performs better than NZP CSI-RS method with practical channel estimation and measurements when the precoded interference is measured.
Transfer Learning with U-Net type model for Automatic Segmentation of Three Retinal Layers In Optical Coherence Tomography Images

Retinal layer analysis on OCT images is a standard procedure used by ophthalmologists to diagnose various diseases. Due to a large number of generated OCT images for each patient, a manual image analysis can be time-consuming and error-prone, which can consequently affect the timeliness and quality of the diagnosis. Therefore, in recent years, a variety of methods, based prevalently on deep learning, have been proposed for the automatic segmentation of retinal layers. In our study, the U-Net type model with a ResNet based encoder, pretrained on ImageNet dataset is utilized. In addition, the model is combined with postprocessing step to obtain segmented layer boundaries. The modified versions of U-Net type model have already been applied to various non-medical imaging segmentation tasks, achieving outstanding results. To investigate whether the pretrained U-Net type model contributes to improvement of retinal layer segmentation, two models are trained and validated on 23 volumes of OCT images with age related macular degeneration (AMD): the U-Net model with pretrained ResNet34 encoder on ImageNet dataset and the original U-Net model, trained from the scratch. The one-sided Wilcoxon signed-rank test has shown that the pretrained U-Net type model outperforms the original U-Net model for segmenting three regions bounded by four layer boundaries.
Compact Microstrip Lowpass Filter with High Harmonic Suppression

A planar lowpass filter designed using two resonators of different shape and four high-low impedance transmission lines being used as suppressing cells is realized with wide stopband and compact size. The proposed filter is found to remove up to seventh harmonics. The influence of each resonator on the design is studied separately. The cutoff frequency of the proposed filter is 2.57 GHz and its physical size is 14.6 mm x 9.6 mm. The selectivity is 92.5 dB/GHz. The measured stopband at 18 dB attenuation level is observed from 2.78 GHz to 20 GHz. Simulated results are validated with the measurement results and a good matching is observed between them.
Iterative Learning Control for the Shank Part of Lower Limb Exoskeleton

This paper briefly introduces the relative motion control problems of the wearable exoskeleton robot. By approximating the ideal human gait as a periodical signal, an iterative learning control algorithm with a D-type updating law is applied to the shank part of the exoskeleton robot to solve the tracking problem. As the non-linearity in the dynamic model of this exoskeleton robot is leading to a degradation in the efficiency of normal iterative learning methods, a feedback linearization method is introduced to improve the overall performance of the algorithm. Finally, the effects of measurement noises to the algorithm are discussed.
Analysis of TSN for Industrial Automation based on Network Calculus

Time-Sensitive Networking (TSN) Ethernet is becoming a primary industrial networking technology, since it provides bounded latency capability with broad reaching ecosystem, supporting both real-time communications and non-time-critical communications in a factory. In TSN, QoS-based shaping is a core technology to provide bounded latency. Network calculus is a theory which aims at worst-case performance analysis in the network. In the paper, latency upper bound of TSN for industrial automation scenario is analyzed based on Network Calculus. Credit-based Shaping (CBS) combined with Strict Priority (SP) is used as the specific QoS-based shaping mechanism of TSN. Relating factors that influence the latency upper bound of TSN are also studied.
NOMA Enhanced Backscatter Communication for Green IoT Networks

Backscatter communication has recently emerged as a promising technology to enable the passive sensing-based Internet-of-things (IoT) applications. In a backscatter communication network, uplink transmissions of multiple nodes are usually multiplexed in time- or frequency-domain to avoid collisions, yet it is desirable to improve the uplink capacity further. In this paper, we study a wireless-powered backscatter communication system, where the sensors use a hybrid channel access scheme by combining time division multiplexing access (TDMA) with power-domain non-orthogonal multiple access (PD-NOMA) to enhance the system performance in terms of outage probability and throughput. Our analysis shows that the proposed PD-NOMA increases both the spectrum efficiency and the throughput of the system.
Multipath Based Adaptive Concurrent Transfer for Real-Time Video Streaming Over 5G Multi-RAT Systems

5G systems use multiple radio access technology (multi-RAT) to provide broadband, low latency, and high reliability services. Multi-RAT is effective when multipath transmission is used over multichannels. This is why multipath based transmission is one of the key technologies that can improve the throughput and reliability of real-time video streaming over 5G networks. This paper proposes two multipath based concurrent transfer techniques; fast concurrent transfer (FCT) and reliable concurrent transfer (RCT). FCT aims to minimize the round trip time (RTT) required for frame transmission by arranging each of the packets constituting a frame to be sent over multiple channels. In an environment where there are multiple channels, RCT performs redundant transmission of packets, so that even if a packet is lost in one path, the lost packet can be received safely over another path. FCT aims at securing the reliability of data transmission. In addition, a multipath based adaptive concurrent transmission (MACT) scheme is proposed which controls the ratio of FCT and RCT adaptively considering the state of the receiver buffer, inflow throughput, and decoding rate to enhance the reliability and throughput performance. The simulation results show that the proposed MACT scheme results in a better performance compared to the conventional transmission schemes in various network channel conditions.
Path-Tracking for Autonomous Vehicles with On-line Estimation of Axle Cornering Stiffnesses

To improve the accuracy and stability of path tracking under critical driving maneuvers, we propose a novel path tracking method for four-wheel-independent-drive electric vehicles (4WID-EV). An adaptive prediction model is applied to deal with the strong nonlinearity and time-varying behaviors of tires under critical driving maneuvers. The instantaneous cornering stifffiesses of the prediction model is estimated by an online estimator which combines unscented Kalman Filter (UKF) with recursive least square method with adaptive forgetting factor (adaptive FF-RLS). In addition, to handle the dynamics constraints under critical driving maneuvers, the model predictive control with multi-constraints (MMPC) is proposed to calculate the desired steering angle. The joint simulation results under two scenarios indicate that the proposed controller can minimize the path tracking deviations compared with the normal MPC controller, and maintain the stability of autonomous vehicle at high speeds.
Event-triggered control for a class of second-order uncertain p-normal nonlinear systems

The problem of event-mggered control for a class of second-order uncertain nonlinear systems subject to A novel in this paper is that the input-to-state stability (ISS) assumption is removed. By adding a power integrator technique, we design a virtual control signal to address the uncertainties and nonlinearities of nonlinear systems. Through rigorous stability analysis, all the signals of the closed-loop system are proven to be globally uniformly bounded and can converge to an arbitrarily small set which can be be adjusted by one parameter. A numerical example demonstrates the effectiveness of the designed scheme.
A real-time virtual manipulator simulation platform based on FPGA

The high-performance auxiliary manipulator has incomparable advantages for the rehabilitation training of people with dyskinesia. As rehabilitation training tasks become more complex and real manipulators have high costs, researches on convenient and low-cost simulation technology have great significance. Traditionally, the simulation of the manipulator is mostly based on software, which has low efficiency and the application prospect is not as wide as the hardware. Compared with software simulation, field-programmable gate array (FPGA) shows more advantages especially in the real-time simulation. In this paper, a proportional-derivative (PD) controller is designed based on the two-degree-of-freedom (2-dof) manipulator model, and the hardware is implemented on the FPGA chip. By comparing MATLAB simulation results with hardware implementation results, the simulation platform is verified to be high-performance in real-time computational power, which meets the requirement for the high-speed control in rehabilitation applications. This platform is helpful for rehabilitation training of patients by using mechanical devices and has potential application prospect in the field of real-time brain-computer interface (BCI).
Compact Real-Time Terahertz Spectroscopy Based on Quantum Cascade Lasers

We demonstrate a compact terahertz multiheterodyne dual-comb spectroscopy using two quantum cascade lasers (QCLs) without a need of additional fast detectors. With only $\sim 270$ nW terahertz power coupled into a terahertz QCL detector comb (no optics for alignment), the down-converted dual-comb spectra are successfully obtained in real-time at different carrier frequencies. To prove the spectroscopic ability, we further demonstrate that the compact dual-comb system can be used to calibrate the relative humidity in the air and to measure the transmission of samples. Due to the small optical coupling aperture (150 $\mu$ m), it is also potential to use the dual-comb technique for terahertz imaging.
Characterization of a radial cylindrical spring using an electromechanical test bench

The Center for Artificial Muscles of EPFL in Neuchâtel focuses his research on designing a less invasive cardiac assist device based on a dielectric elastomer actuator. The idea is to place a ring around the aorta and activate it to help failing heart pumping. To enhance the performances of the actuator, in terms of both force and displacement, a cylindrical negative stiffness spring has been designed and manufactured; the latter being combined with the dielectric elastomer actuator. The aim of this paper is to propose an innovative set-up to validate the theoretical characteristic of this particular spring. This is performed by means of an electromechanical test bench that allows to radially compress the spring and measure the resulting characteristic. The test bench is composed of 16 fingers that are responsible for radially compressing the spring. A force sensor is integrated in one finger and a laser position sensor is used to measure the deformation. A user interface is built on LabVIEW in order to perform the measurement process. A first experiment is carried out with a planar negative stiffness module in order to validate the concept of the test bench with a simple sample. In the second part of the experiment, the cylindrical spring is tested. In both experiments, the negative slope in the force displacement characteristic is observed.
Propagation of Harmonics of Return Traction Current in Rail lines

An investigation of propagation of return traction current harmonics in rail lines was carried out for railway sections with d. c. and a. c. traction. A mathematic model of traction supply net was proposed. The improved mathematical model of traction supply net allows us to estimate the propagation of harmonics and traction return current along the feeder zone with different kind of power supply system. The impedance of a power network was determined for low frequency interferences of return traction current. Also this paper deals with the determination of spectrum composition of traction current and most dangerous harmonics for track circuits.
Terahertz generation by Kerr effect at metal surfaces

The emission of terahertz radiation from laser-excited thick metal samples through Kerr-like effect has been detected for the first time to the authors’ knowledge. The observed THz waveforms are generated through nonlinear electronic processes at surfaces (optical rectification) and in the bulk (four-wave mixing) of gold sample.
Review on Various Emotional Disorders by Analyzing Human Brain Signal Patterns (EEG Signals)

Making machine more emphatic to the user is the gorgeousness of emotive computing. To look inside the users head and operate according to the observed mental state is the ability of emotion identification machines. Now a day’s lot of research is going on in e-healthcare systems. By means of bio-sensors we can capture brain signals, which are used to discover human emotional and mental state. As emotions are playing very important role in making decisions and other mental tasks. Such e-healthcare systems are really required to improve overall human’s life. As researchers are showing lots of interest in establishing emotional interaction between human and machine (HCI) and for this recognition of emotional state is basic requirement.In this paper, various human emotions and EEG signal patterns of human brain are studied. Here we present analysis on neurophysiologic research which can be performed by various researchers. The EEG signals are helpful to analyze various human emotions. Emotion is important indicator of mental instability. The objective of this paper is to find out how these emotions are helpful to solve emotional disorders like depression, anxiety and emotional instability.
A DCNN-based Arbitrarily-Oriented Object Detector for a Quality-Control Application

Following the success of machine vision systems for on-line automated quality and process control, in this paper we describe an object recognition solution aiming at detecting the presence of quality control elements in surgery toolboxes prepared by the Sterilization Unit of a hospital. Our solution actually consists in a two-stage arbitrarily-oriented object detection method making use of indirect regression of oriented bounding boxes parameters. The paper describes the design process and reports on the results obtained up to date.