Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk-Aware Multi-Armed Bandits for Vehicular Communications

The importance of vehicular communications has grown significantly in recent years. Potential use cases of vehicular communications are manifold and range from sharing information for driver assistance to entertainment purposes. This means that each connected vehicle has an individual data requirement for the communication infrastructure. However, due to the dynamic wireless environment, the simultaneous fulfillment of such requirements cannot be guaranteed. Therefore, novel solutions should not only consider the requirements of each user but also the risk of not being able to fulfill them. In this paper, we consider a vehicular communication scenario consisting of a base station that serves the vehicles in its coverage area using 5G millimeter wave (mmWave) narrow beams. The problem boils down to finding an optimal policy for the selection of the narrow beams. This should be done carefully, as the choice of the used beams greatly impacts the performance. For this purpose, we propose a risk-aware contextual Multi-Armed Bandit (MAB) online learning algorithm. Using this algorithm, the base station autonomously learns its environment and selects the best set of beams based on the vehicles located in its coverage area. In order to achieve a large risk awareness, this work focuses on two pillars. Firstly, the notion of risk is integrated in the proposed contextual MAB algorithm by exploiting the concepts of Mean-Variance and Conditional Value at Risk for the evaluation of the decisions made by the algorithm. Secondly, we introduce mechanisms that can detect non-stationarities and swiftly adapt to them in order to make the proposed approach robust against volatile environments that violate stationarity assumptions. By using extensive simulations, the effectiveness of the aforementioned approaches are proven numerically.
A DC-to-18 GHz SP10T RF Switch Using Symmetrically-Routed Series- TL-Shunt and Reconfigurable Single-Pole Network Topologies Presenting 1.1-to-3.2 dB IL in 0.15 $\mu$ m GaAs pHEMT

As a rule of thumb, the number of throws, bandwidth (BW), and insertion losses (IL) limit each other in a single switch circuit. This paper proposes three topologies, i.e. symmetrically-routed structure, series-transmission-line-shunt unit, and reconfigurable matching network, to breakthrough these limits. Demonstrations show this work attains a single-pole ten-throw (SP10T) switch and achieves an impressive BW from DC-to-18 GHz with a favourable IL performance (1.1-3.2 dB) compared to prior arts. RF input power for 1-dB compression is 21.2 dBm, and isolation is higher than 24 dB.
CRC-Aided Adaptive Belief Propagation Decoding of NR LDPC Codes

In this paper, we focus on how to further improve the performance of short block-length low-density parity-check (LDPC) codes, especially the short LDPC codes in 5th generation (5G) New Radio (NR) systems. We propose an enhanced adaptive belief propagation (ABP) decoding algorithm with the assistance of partial cyclic redundancy check (CRC) bits. Meanwhile, the error detection ability can still be guaranteed by the remaining CRC bits and adaptive decoder parameters. Moreover, the proposed partial CRC-aided ABP (CA-ABP) decoding can be combined with the conventional BP decoding to further improve the performance. Simulation results show that compared with the BP decoding our proposed decoding method achieves an improvement in the error-correction performance.
On Performance of multi-user Massive MIMO for 5G and Beyond

5G New Radio (NR) is the latest radio access technology (RAT) developed by 3GPP for the 5G mobile network. 5G NR and beyond is expected to play a key role in Cyber-Physical Systems as it will deliver significantly faster, more reliable and much lower latency connections to enable wireless control applications. 5G will support three fundamental application scenarios, enhanced Mobile BroadBand (eMBB), Ultra-Reliable and Low deployment Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). mMTC is of particular importance as it forms the basis of IoT, whereas URLLC will support mission-critical applications such as autonomous robotics. The commercial roll-out of 5G is planned in phases with challenging new vertical deployments as the technology is still evolving and little practical experience is available yet. Massive MIMO is a vital enabling technology for 5G NR, enhancing reliability and data rates in challenging environments. It is one of the technologies having a low carbon emission rate as it exploits the resources in an optimal way, hence enabling more sustainable and greener networks. In this paper, we investigate the performance of two MIMO precoding techniques in terms of achievable sum rates for massive MIMO. Simulation experiments show that Zero Forcing (ZF) precoding outperforms Maximum Ratio Transmission (MRT) precoding for the given scenario and assumed conditions.
A Scalable LDPC Coding Scheme for Adaptive HARQ Techniques

In wireless communication systems, the achievable data rates are always dependent on the characteristics of the wireless channel. Although a system is designed for an expected channel link quality, the channel may deteriorate for certain periods of time due to the changing nature of the propagation environment. A weak channel may lead to transmission errors, which can be corrected by employing Hybrid Automatic Repeat Requests (HARQ). In particular, type II HARQ protocols have become an appealing option since they only involve the transmission of additional parity bits, instead of repeating the original transmission. These additional parity bits can be obtained by puncturing a low rate channel code. However, finding an optimum pattern of puncturing for a specific code is non-trivial. In this work, we propose a retransmission scheme that considers various Low-Density Parity-Check (LDPC) codes of different rates that can be concatenated to perform the encoding and decoding operations, resulting in a lower encoding overhead for successful transmissions, while maintaining efficient decoding. We show that this technique can maintain high throughput and goodput, and a low outage probability, even when the received signal power is suddenly reduced due to the loss of the Line-of-sight (LOS) component.
Design an Efficient Cuk Converter for a BLDC Motor Drive

In this research work, designing of Cuk converter which operated in a closed loop using PI controller technique to maintain the respective reference voltage has been carried out. The regulated voltage obtained at the DC link capacitor terminals is then provided to the voltage source inverter (VSI) in order to feed the load. The Cuk converter generated inverting output. A BLDC motor is considered as a load in this research work. The Cuk is a type of DC-to-DC converter that either increases or decreases the output voltage according to our needs. In addition, Cuk converter provides advantages like high efficiency, having simple configuration, not having any transformer hence low losses, operates on high frequency and easy control strategy. In this paper, the performance of BLDC motor is analyses on the basis of its transient performance, steady state performance and dynamic performance. These results are generated using MATLAB/Simulink.
Attacker Identification In LoRaWAN Through Physical Channel Fingerprinting

In today’s environment, wireless networks and IoT technologies have been integrated into a variety of applications. One of the most essential IoT technologies is the Long-Range Wide Area Network (LoRaWAN) because of its capacity to combine long-range communication with energy-efficiency. Assuring network traffic safety and security is a critical issue in a wide range of today’s industries, and security is a critical feature now that the digitization is accelerating. The ”Man in the Middle” attack is one of the most dangerous and difficult to identify threats to wireless networks, and may become a critical threat for digitized assets and industrial processes. In this study, we look at an alternative approach to resolving this issue in order to assure the security of wireless networks. The technique is based on the ”Behavioural Security Method,” in which we employed a Feed-forward neural network model to build a binary classifier that can discriminate and recognize the original desired data while detecting the attacker’s data. As a result, we discovered that as long as the attacker and the targeted node are not co-located, the model can discriminate between data coming from the source and data coming from the attacker with 99.6 percent accuracy. If, on the other hand, they are co-located, the model will fail to identify the source of data and the model’s accuracy will drop to roughly 50%.
Spatial Degrees of Freedom for Physical Layer Security in XL-MIMO

This paper analyzes the key differences between the plane-wave (PW) and the spherical-wavefront (SW) models in the context of physical layer security (PLS) for extra-large (XL)-multiple-input multiple-output (MIMO) systems with multiple eavesdroppers. Both colluding and non-colluding strategies are considered for the different eavesdroppers. Results show that the propagation features associated to the SW model allow to reach higher secrecy rates, and to reduce the areas where an eavesdropper makes secure communication unfeasible.
Real-time and multi-layered energy management strategies for fuel cell electric vehicle overview

In this paper, energy management strategies (EMS) applied to fuel cell electric vehicles (FCEV) are discussed toward a promising new global architecture. Beyond fuel consumption saving, we aim to improve flexibility to the real-time driving conditions while meeting the constraints of the hybrid energy storage system. To achieve the EMS real-time implementation, vehicle topology and energy sources must first be analyzed. Then, EMS applied to FCEV challenges and constraints can be presented. A classification of the leading existing EMS is established, which explains the relevance for real-time implementation of an integrated EMS (iEMS) architecture based on a hybridization concept, using rule-based, optimization-based, and learning-based strategies simultaneously. Finally, it is shown that data processing algorithms such as driving pattern recognition and driving cycle prediction must also be integrated into real-time driving condition EMS.
GBHO: A Gain-Based Heuristic Offloading Algorithm in Vehicular Edge Computing

Energy saving and Quality of Service (QoS) are essential for task offloading and resource management in the vehicular edge computing (VEC) system. However, the dynamic feature of vehicular environment brings difficulties to obtain a proper strategy for energy consumption minimization while satisfying the QoS requirement. To handle these difficulties, in this paper, we investigate the problem of designing a joint task offloading and resource management scheme to minimize energy consumption under QoS requirement in VEC. Compared with previous researches which set the value of instant data rate as the QoS requirement, we consider the constraint of time average data rate under the fact that some wireless applications allow some latitude in QoS requirement. The formulated multi-stage problem is transformed into a series of single time slot equivalent sub-problems through Lyapunov optimization. Furthermore, a gain-based heuristic offloading algorithm (GBHO) is proposed to obtain a proper solution of the sub-problems. Simulations illustrate that GBHO reduces by 46.4% around in energy consumption compared with instant QoS constraint strategy while satisfying the QoS requirement. Moreover, the processing time of GBHO reduces by 91.8% around of that of the traditional concave-convex procedure approach.
LSTM-based Spectral Efficiency Prediction by Capturing Wireless Terminal Movement in IRS-Assisted Systems

For wireless communication in the high-frequency band, Intelligent Reflecting Surface (IRS) has been developed to expand the coverage. To appropriately control the reflection pattern of each element in the IRS, deep learning (DL)-based spectral efficiency predictions have been proposed. The conventional method performs prediction from the partially estimated channel at a single point in time. However, since the movement of wireless terminals is spatially continuous, the accuracy can be improved using past estimated channels. Therefore, in this paper, we propose a prediction method that considers the movement of wireless terminals by treating the estimated channel as time-series data. Furthermore, we apply a long short-term memory network to capture the time-series nature efficiently. The numerical experiments show that the proposed method can achieve high spectral efficiency even with smaller training samples than the conventional method.
Transmit Antenna Selection and Artificial Noise Design for Secure STBC-SM Transmission

In this contribution, for enhancing the secure transmission of space-time block coded spatial modulation (STBCSM) systems, we conceive a minimum Euclidean distance (ED) transmit antenna selection (MED-TAS) method in conjunction with a new artificial noise (AN) design based on the optimized ED criterion (ED-AN). Specifically, we select the antennas by taking both legitimate receiver and eavesdropper into consideration, while proposing optimized ED-AN by minimizing the ED between the two specified signals, in order to enhance the performance at the legitimate receiver while degrading that at the eavesdropper. Simulation study demonstrates that the proposed technique is capable of achieving better security performance compared to its counterpart as conventional AN-aided STBC-SM design.
A Wind Diesel Hybrid System Simulation for Power Generation using PMSG

The limited availability of conventional sources of energy and aggravated demand lined up a way for renewable energy sources. A Hybrid system employs one renewable energy source integrated with other conventional or non-conventional sources. A Wind-Diesel Hybrid System (WDHS) model has been presented in this paper. It presents an approach to developing active power at a time when wind independently can't gratify the energy demand, and diesel jointly with it generates and supplies to the grid. For conversion of mechanical energy to electrical a permanent magnet synchronous generator (PMSG) has been employed instead of Induction Generators. The whole model has been developed in MATLAB/Simulink environment. Along with designing the performance analysis of the WDHS has been done. The active power output obtained across the load due to WDHS is coming to be 2 KW whereas the reactive power of the system is coming to be 3 KVAR after a certain droop for a fixed load of 2KW which can be seen through the graphs.
An Adaptive Integral Sliding Mode Control with Allocation Scheme for Reconfigurable Flight Array

This paper proposes a tracking control strategy for a reconfigurable flight array system with varible configuration. The presented control scheme does not dependent on the accurate mathematical model of system. By employing a pseudo-inverse method with the control efficiency matrix, an control allocation method is constructed to achieve trajectory tracking for a multi-configuration flight array system. To maintain the trajectory tracking performance, an adaptive integral sliding mode controller is designed by using the distance information of the sliding variables and the boundary layer. Theoretical analysis is studied in terms of the Lyapunov theory. Finally, comparative simulation results are provided to demonstrate the effectiveness of the proposed method.
Application of Feedforward Compensation in the Design of Active Front-End Converters

A voltage-sourced current control inverter is the building block of an Active Front-End converter (AFE). AFEs which are more commonly known as smart-inverters, are ever increasingly being used in electric vehicle-chargers, renewables and power conditioning systems. This paper presents a control method that improves the performance of the voltage-sourced current controlled inverter. The proposed control method improves the undesirable start-up transients and disengages the dynamics of the inverter from those of the AC grid with which it interfaces. These features allow the inverter to achieve better performance with greater longevity. A comparison study is provided to show the advantages and disadvantages of the proposed method with respect to conventional alternatives in the literature. The analytical results and the conclusions are validated by testing the proposed model in different scenarios. The enhanced control system is also put to test on a quadrature model of a practical AFE that utilizes a 12 pulse Neutral Point Clamped inverter.
FloodDAN: Unsupervised Flood Forecasting based on Adversarial Domain Adaptation

As a natural disaster, floods can cause enormous social and economic losses. However, both physical models and learning-based flood forecasting models require massive historical flood data to fit the model parameters. When come to new site that does not have sufficient historical data, the model performance will drop dramatically due to the overfitting problem. In this paper, we propose a Flood Domain Adaptation Network (FloodDAN) that is able to perform unsupervised flood forecasting. Specifically, we design a two-stage learning procedure to train our FloodDAN: in the first stage, we pretrain a rainfall encoder and a prediction head to learn general transferable hydrological knowledge on large-scale source domain data; in the second stage, we transfer the knowledge in the pretrained encoder into the rainfall encoder of target domain through adversarial domain alignment. During inference, we utilize the target domain rainfall encoder trained in the second stage and the prediction head trained in the first stage to get flood forecasting predictions. Experimental results on Tunxi and Changhua flood dataset show that our proposed FloodDAN can perform flood forecasting effectively with zero target domain supervision. The performance of the FloodDAN is on par with supervised models that uses 450–500 hours of supervision.
Perceptual-Aware and Restorable Real-Time Image Downscaling

Image downscaling has been a classical problem and has recently been linked to super-resolution (SR). In this paper, we aim to propose a learning-based image downscaling model, FastDownscaler, which can efficiently produce low-resolution (LR) images that not only preserve the rich details of the original high-resolution images but also be highly restorable for existing SR models. We first present two separate lightweight networks with different upsampling losses, the bilinear loss and the bicubic loss, which are better for SR restoration and LR downscaling, respectively. To produce versatile LR images, we then propose to distill bilinear loss guided network with bicubic loss guided one. To our best knowledge, we establish the first image downscaling quality assessment dataset to evaluate the downscaling performance. Experimental results demonstrate the superior performance of the proposed model on image downscaling and SR. Furthermore, our model can achieve over 600 FPS for downscaling a $1920\times 1280$ image.
Exploiting Implicit OVSF Structure in DM-RS for Improved Channel Estimation in 5G NR Systems

The Demodulation Reference Signal (DM-RS) pilot pattern in 5G NR uses Orthogonal Cover Codes (OCC) and frequency orthogonal pilot positions to enable channel estimation of different users or spatially multiplexed layers of the same user. These OCCs utilize Walsh-Hadamard codes in a pilot-on-pilot arrangement to introduce code orthogonality. Our work reveals how the choice of the OCC and frequency position of the pilot sequence can improve the channel estimate quality. The key contributor to this improvement is an implicit pattern in the OCC structure which derives from the theory of Orthogonal Variable Spreading Factor codes. With the help of an appropriate denoising and interpolation scheme, the proposed estimation scheme is shown to significantly reduce the Mean Square Error of the channel estimates for various DM-RS configurations.
Trajectory Tracking of Bus Based on Feedback-feedforward Model Free Adaptive Control

In this paper, a feedback-feedforward trajectory tracking control method based on model-free adaptive iterative learning is proposed for bus that aiming the periodic repetitive work characteristics. First of all, considering the bus operation reality, the vehicle longitudinal kinematics model is established under repetitive environment. Secondly, the model free adaptive control (MFAC) is combined with feedforward iterative learning control (ILC) to realize the vehicle position tracking. Finally, the simulation analysis is carried out with selected bus parameters. The simulation results show that this control method can effectively utilize the historical data of the bus operation, and realize the high-precision tracking performance in the limited time range.
Bird Species Classification Using Deep Learning

Many bird species are becoming more difficult to locate, and even when they are, it may be difficult to anticipate their classification. Observed from a distance, birds may be seen in a wide range of sizes, shapes, colors, and orientations. The photographs show a great deal more variation in the bird's breed than the auditory categorization. Using photos, people are better able to discriminate between birds. Because of this, the Caltech-UCSD Birds 200 dataset is used for both training and validation in this technique. Deep convolution neural networks (DCNN) are used to turn an image into a grayscale representation, while the tensor flow is used to construct an autograph with a large number of nodes that can be compared. A rating table is developed as a consequence of evaluating the numerous entry points to the validation data. By examining the scoreboard and selecting the highest rating, it may be able to anticipate the required bird group. A look at the dataset (CUB-200-2011) reveals that the system has a bird identification accuracy of 89 percent. Linux and the Tensorflow framework were used in the study.
An 88.9-dB SNR Fully-Dynamic Noise-Shaping SAR Capacitance-to-Digital Converter

This article presents an energy-efficient noise-shaping successive-approximation register (SAR) capacitance-to-digital converter (CDC) for high-resolution capacitive sensor applications. Based on a 12-bit SAR architecture, quantization noise is shaped by the first-order FIR-IIR loop filter. The proposed loop filter comprises dynamic amplifiers (DAs), and it is designed to be insensitive to DA gains’ variations. Under process, voltage, and temperature (PVT) variations, the loop filter is stable and retains in-band noise suppression ability without the gain calibration. The CDC is implemented in a differential configuration for a single-sensor measurement with an energy-efficient capacitive digital-to-analog converter (CDAC) switching method. It does not require any replica or dual capacitor sensor for differential operation. In the proposed CDC, a dynamic comparator with a common-mode (CM) rejection is proposed to compensate for the instability because the parasitic capacitance causes CM voltage deviation of the CDAC and loop instability. The proposed CDC is fabricated in a 180-nm CMOS technology with an active area of 0.25 mm2. It dissipates 63.3 $\mu \text{W}$ at a sampling frequency of 320 kHz. Given that all circuits operate dynamically, the sampling frequency is scalable from 3.2 to 320 kHz. The prototype CDC achieves an 88.9-dB signal-to-noise ratio (SNR) and a figure-of-merit of 139 fJ/conversion-step.