Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of Correlated Blockage in Highway Vehicular Networks

This paper presents a novel spatially consistent approach for addressing blockage and line-of-sight (LOS) paths in highway vehicular networks. We use stochastic geometry to model transmitters, obstacles, and receivers located in three lines, respectively. Then, their geometric interactions characterize the existence of LOS paths. Specifically, the proposed approach focuses on the role of obstacles in blocking one or more LOS paths, which has been overlooked in most statistical models for blockage. Under the proposed framework, we derive the probability that a typical vehicle is in LOS with respect to transmitters. The proposed framework and LOS analysis are important to the LOS-critical applications such as positioning or mmWave communications in vehicular networks.
Energy-Efficient Solutions in Two-user Downlink NOMA Systems Aided by Ambient Backscattering

In this paper, the energy efficiency of a two-user downlink NOMA system aided by several ambient backscatter devices is investigated. We analyze both the tradeoff and the ratio between achievable rates versus power consumption, assuming that the backscatter devices are in fully cooperative mode. In the case of two backscatter devices, we derive a closed-form solution in terms of the optimal reflection coefficients and power allocation policy by exploiting the properties of the energy-efficiency objective and the Pareto boundary of the feasible set. For more than two backscatter devices, the problem becomes difficult and our methodology cannot be extended easily. Nevertheless, we evaluate the performance of NOMA aided by several (up to four) backscatter devices via numerical simulations. Our numerical results show that the energy efficiency of the two-user NOMA system increases with the number of cooperative backscatter devices. Moreover, in the high noise regime, the relative efficiency gain increases with the number of backscatter devices reaching up to 370 % compared to conventional NOMA.
Age-of-Information-Aware Digital Twin Assisted Resource Management for Distributed Energy Scheduling

Digital twin (DT) provides a real-time digital representation of electric device state for energy dispatching and control (EDC) model training in power system. However, the large age of information (AoI) deteriorates the consistency of DT and the precision of EDC model. In this paper, we investigate the global loss function minimization problem underthe long-term AoI constraint through coordinated resource management. The optimization problem is decoupled based on telescoping sum and Lyapunov optimization, and solved by the proposed AoI-aware DT-assisted intelligent resource management algorithm named AoI-DT. AoI-DT achievesa balanced tradeoff between AoI guarantee and EDC model precision through device scheduling and channel allocation. Simulation results verify the superior performance of AoI-DT in terms of global loss function and AoI compared withtwo state-of-the-art algorithms.
Intrusion Detection Model Updates Through GAN Data Augmentation and Transfer Learning

Current machine learning techniques for network-based intrusion detection cannot handle the evolving behavior of network traffic, requiring periodic model updates to be conducted. Besides requiring huge amounts of labeled network traffic to be provided, traditional model updates demand expressive computational costs. This paper proposes a new feasible model update procedure implemented in two steps. First, we use a Generative Adversarial Network (GAN) to augment the sampled network traffic. Next, we use the augmented dataset to perform model updates through a transfer learning-based approach. Thus, our model can decrease both the number of instances that must be labeled and the computational costs during model updates. Our experiments on a one-year dataset with over 8 TB of data show that literature techniques cannot handle changes in network traffic behavior. In contrast, the proposed model without updates improved true-positive rates by up to 25.6%. With monthly model updates, it requires only 14% of computational costs and 2.3% of instances to be provided.
Privileged Estimate Fusion With Correlated Gaussian Keystreams

Providing cryptographic privacy guarantees in a distributed state estimation problem has been a growing topic of research since the ubiquity of modern public networks. One such guarantee is having different levels of estimation performance achievable by trusted and untrusted users within a sensor network. In the presence of multiple sensor measurements, guaranteeing better estimation performance by the usual means of adding removable noise to measurements is complicated by an alternative for untrusted users to improve their performance: fusing more measurements. Our novel method adds correlated noise to different sensors, restricting the performance gained from fusing additional measurements while guaranteeing better performance to those that can remove it. We extend a cryptographic framework for defining estimation privilege and use this to prove the scheme’s security goals, while simulations demonstrate the effects of parameters in a concrete estimation scenario. A scheme that can ensure such differences in estimation performance between estimators of differing privileges can find applications in priority-based or subscription-based estimation performances in environments where more than one sensor is present.
Shared Control of Robot Manipulators With Obstacle Avoidance: A Deep Reinforcement Learning Approach

The word <italic>teleoperation</italic> (which, in general, means “working at a distance”) is typically used in robotics when a human operator commands a remote agent. A teleoperated robot is often employed to substitute human beings in conditions where the latter cannot operate. A possible reason is the need to be in contact with dangerous substances, and indeed, the first robot teleoperation system was designed in the 1940s for handling nuclear and chemical materials <xref ref-type="bibr" rid="ref1">[1]</xref>. Other reasons can be the difficulty in bringing people on missions to explore deep waters or space <xref ref-type="bibr" rid="ref2">[2]</xref>, <xref ref-type="bibr" rid="ref3">[3]</xref> as well as the need to work with very high precision (for example) during a surgery <xref ref-type="bibr" rid="ref4">[4]</xref>, <xref ref-type="bibr" rid="ref5">[5]</xref>. In certain cases, the reference provided by the human operator is not directly passed to the robot but is instead used to generate an adaptive motion. This approach is known as <italic>semiautonomous teleoperation</italic> or <italic>shared control</italic> <xref ref-type="bibr" rid="ref6">[6]</xref>, and its aim is to reduce the workload of the human operator during the performance of a difficult task that involves controlling a robotic system.
Printed Quarter-Wavelength Absorber Using Carbon Nanotube Ink

In this paper, a quarter-wavelength absorber composed of printed resistive and reflective sheets is realized. The sheet resistances are 342 and $0.5 \Omega/\text{sq}$. using 1.7 times diluted carbon nanotube ink and silver copper ink. The wave absorber with −20 dB fractional bandwidth of 17% at the center frequency of 19.7 GHz is fabricated. The lightfastness characteristics of the fabricated wave absorber are also evaluated for outdoor use scenarios and the change of the fractional bandwidth was less than 2%.
Development of an Autonomous Mobile Robot in the Outdoor Environments with a Comparative Survey of LiDAR SLAM

A high-precision mapping is one of the essential requirements for safe autonomous driving because it is directly connected with the accuracy and safety of autonomous driving. Due to the development of 3D light detection and ranging (LiDAR) sensor technology, LiDAR is playing a key role to create the precise maps. Although many open-source-based LiDAR simultaneous localization and mapping (SLAM) algorithms have been developed, their accuracy in actual experiments is often not proven. To handle this issue, our paper aims to provide background materials for autonomous robots and experimental validations of open-source-based LiDAR SLAM. In the experiments, the comparison results with four state-of-the-art LiDAR mapping algorithms are described including LiDAR odometry and mapping(LOAM) and LiDAR inertial odometry mapping.
Guaranteed safe control of systems with parametric uncertainties via neural network controllers

Deep neural networks have become a common method to obtain an explicit approximation of the solution of model predictive control problems. The explicit nature of neural networks makes possible to implement near-optimal complex control algorithms on a wide range of hardware. We introduce mixed-integer problems that enable analyzing the behavior of the closed-loop system consisting of the highly nonlinear neural network controller and a linear system with parametric uncertainties. Based on the mixed-integer formulations, an algorithm is developed to verify if the closed-loop operation with a neural network controller guarantees safety for a set of initial conditions. The efficacy and accuracy of the presented approach is investigated via simulation results and parameter studies.
Range-view (RV) Based Semantic Segmentation of Outdoor Point Cloud with Data Augmentation

Semantic segmentation is a critical task in scene understanding of autonomous driving. Because of the irregular and sparse structure of outdoor point clouds, semantic segmentation for point clouds has received a lot of attention from both academia and industry. In this paper, we propose an approach that combines data augmentation for point clouds and lightweight 2D semantic segmentation network. The data augmentation technique produces a balanced dataset for training by interpolating more samples of object classes. The 2D deep layer aggregation network is then employed to train a semantic segmentation model on above augmented dataset to achieve better performance while costing less memory. We benchmark our model on the NuScenes dataset against RangeNet++. Our experiments demonstrate a +1.8% rise in mIoU and an over 6-fold reduction in trainable parameters compared to the baseline.
Discrete-Time Adaptive PID Current Controller for Wind Boost Converter

This paper proposes an adaptive controller for a dc-dc boost converter to regulate the output current levels of a 400 W small wind turbine based on a permanent magnet synchronous generator with a three-phase diode rectifier. Despite the stochastic nature of wind, this discrete-time adaptive PID controller regulates the current levels without large overshoots and oscillations to attain the maximum power point of the wind system. The adaptive mechanism algorithm operating with two electrical measurements adjusts the controller parameters, and is designed with an approach consisting of the integration of the variable operation point of the small wind turbine into the discrete-time model of the dc-dc boost converter and the Bode stability criteria. The mechanism algorithm features a low computational complexity for controller parameters estimation and therefor a practical implementation in a low-end microcontroller (small size, low cost, and low energy consumption) is possible. Furthermore, the results based on the processor-in-the-loop simulation approach show that controlling the current levels and the maximum power point of the wind system via the proposed adaptive controller is more effective than a controller tuned around a particular fixed operating point.
Generic Hardware Realization of K Nearest Neighbors on FPGA

K Nearest Neighbors (KNN) algorithm is a straight-forward yet powerful Machine Learning (ML) tool widely used in classification, clustering, and regression applications. In this work, KNN is applied, with three distance metrics, to classify different datasets, experimentally testing each distance metric effect on the classification performance. A static K is applied for the whole dataset optimally chosen based on a 5-fold cross-validation. A reconfigurable hardware realization on field programmable gate array (FPGA) of each distance metric applying selection sort algorithm is proposed. The FPGA realization reaches a throughput up to 4.44 Gbit/sec while only occupying 1% of the Genesys 2 Kintex-7 board area. The algorithm managed to classify all the tested datasets with above 90% accuracy.
Fusing concurrent visible and infrared videos for improved tracking performance

Abstract. Visible and infrared video cameras are the most common imaging sensors used for video surveillance systems. Fusing concurrent visible and infrared imageries may further improve the overall detection and tracking performance of a video surveillance system. We performed image fusion using 13 pixel-based image fusion algorithms and examined their effects on the detection and tracking performance of a given target tracker. We identified five fusion methods that produced significantly better performance, three of which also managed to achieve that with a relatively high efficiency.
Deep Reinforcement Learning for Data Freshness-oriented Scheduling in Industrial IoT

Making a timely and precise scheduling in Industrial Internet of Things (IIoT) is fundamental and critical. Recently, Age of Incorrect Information (AoII) is proposed and utilized to measure the timeliness and accuracy of monitoring. In this work, we investigate a multi-sensor update system and leverage AoII to quantify the information freshness. Our goal is to obtain an optimal scheduling policy to minimize the system-wide cost. We first model the source statuses monitored by sensors as Markov chains and the scheduling problem as a Markov decision process (MDP). Due to the heterogeneity of source statuses in IIoT, it is prohibitive to solve the formulated MDP problem by conventional methods. To this end, we make use of a deep reinforcement learning (DRL) algorithm to solve this scheduling problem. Extensive numerical results verify the effectiveness of the adopted DRL algorithm. In addition, comparing to the conventional Age of Information (AoI) oriented method, we find that the AoII oriented method is much more effective, from the perspective of system-wide cost.
Distributed Estimation of Sensor Networks Based on LSTM-Kalman Filter*

This paper studies the distributed estimation problem of sensor networks where the noise parameters are unknown and some sensors cannot obtain the measurements. The expectation maximization (EM) algorithm integrating Kalman filtering algorithm, which is called the EM-KF algorithm, is adopted to sensor networks. Simulation examples are given to illustrate the effectiveness of the algorithm. By using LSTM network, and LSTM-KF algorithm is further proposed to improve the accuracy of EM-KF algorithm.
Towards Spatial Location Aided Fully-Distributed Dynamic Routing for LEO Satellite Networks

As the Low Earth Orbit (LEO) satellite has extremely high moving speed and limited networking resources, designing dynamic routing has become a promising approach to improve satellite communication performance. Due to the hundreds of satellites within a constellation and the complex attributes of each satellite, traditional routing strategies based on centralized paradigm derivation face increasingly complex challenges. To address these issues, this paper jointly optimizes queuing delay and propagation delay by proposing a fully distributed routing algorithm based on deep reinforcement learning. Each satellite builds a partially observable Markov decision process (POMDP) model based on the spatial location and queue length of surrounding nodes and adaptively selects the next hop by calculating the estimated residual propagation delay between the neighboring satellites and the destination satellite. Simulation analysis shows that our proposed method has tremendous advantages and effectiveness.
F2Trans: High-Frequency Fine-Grained Transformer for Face Forgery Detection

In recent years, face forgery detectors have aroused great interest and achieved impressive performance, but they are still struggling with generalization and robustness. In this work, we explore taking full advantage of the fine-grained forgery traces in both spatial and frequency domains to alleviate this issue. Specifically, we propose a novel High-Frequency Fine-Grained Transformer (F2Trans) network which contains two important components, namely Central Difference Attention (CDA) and High-frequency Wavelet Sampler (HWS). The premier CDA module is capable of capturing invariant fine-grained manipulation patterns by aggregating both pixel-level intensity and gradient information of the query to generate key and value pairs. Subsequently, the proposed HWS discards the low-frequency components of wavelet transformation and hierarchically explores high-frequency forgery cues of feature maps, which prevents model confusion caused by low-frequency components and pays attention to local frequency information. In addition, HWS can be employed as a special pooling layer for the F2Trans architecture to produce hierarchical feature representations in the spatial-frequency domain. Extensive experiments on multiple popular benchmarks demonstrate the generalization and robustness of the specially designed F2Trans framework is well-tailored for face forgery detection when confronting the cross-dataset, cross-manipulation, and unseen perturbations.
Recursive Router Metrics Prediction Using ML-based Node Modeling for Network Digital Replica

Future network infrastructures will need to provide network services safely and rapidly under complex conditions that include accommodating many devices and multiple access lines such as 5G / 6G supported by multiple carriers. Further-more, future carrier networks will support network disaggre-gation technologies to leverage best-of-breed technology from different suppliers in accordance with the service requirements. Therefore, the efficiency of the verification needs to be improved for the combinations of a large amount of various network equipment and components constituting the network infrastructure to ensure network quality and reliability for unknown network conditions. The issue focused on this study is how to improve the prediction accuracy for the metrics of black-boxed network nodes when only the network node settings and traffic conditions are known as the external conditions. To address this, here, we propose machine learning based node modeling to improve the accuracy of predicted network node metrics by recursively adding other predicted metrics to the training datasets step by step in accordance with the feature importance. Experimental results show that the coefficient of determination (R2) of router metrics the throughput, the packet loss, and the packet delays, could be improved by using the training datasets including router settings and other predicted router metrics.
Unequal LC-Ladder Divider with Broad/Dual-Band Characteristics Utilizing Two-Frequency Matching

This paper deals with an unequal power divider with two matching frequencies by using LC-ladder circuits. This circuit that is constructed based on the Wilkinson power divider, consists of 9 elements. This circuit can achieve the same or wider bandwidth as the original one by holding the two matching frequencies close together. In order to confirm the practicality in IoT, the design center frequency is set to 920 MHz, and its actual measurement is carried out after circuit pattern design and analysis.
Simultaneous RIS Tuning and Differential Data Transmission for MISO OFDM Wireless Systems

The Reconfigurable Intelligent Surfaces (RIS) constitute one of the prominent technologies for the next generation of wireless communications. They are mainly envisioned to efficiently enhance the signal coverage in cases where the direct communication link is weak or obstructed. Recently, beam training based on codebook selection has been proposed as a low-latency means for tuning the RIS phase profile according to a desired performance metric. However, it requires the transmission of reference signals to measure the performance with different RIS phase configurations available in the codebook, which reduces the spectral efficiency. In this paper, we consider the uplink of a Multiple-Input Signal-Output (MISO) communication system with Orthogonal Frequency-Division Multiplexing (OFDM) and present a novel scheme for simultaneous RIS phase configuration and data transmission. The proposed scheme is based on non-coherent differential modulation, which is deployed both in the beam training and the data transmission phases. In the former phase, it also enables energy measurement for the determination of the best RIS phase profile. Then, in the latter phase, a higher throughput can be established through the high gain reflective link. Our numerical results showcase that our proposal can double the system throughput with lower complexity, as compared to the generic state-of-the-art approach.
Enhancement and tunability of Fano resonance in symmetric multilayer metamaterials at optical regime

Fano resonance (FR) is routinely observed in three-dimensional symmetric metamaterials (MMs) consisting of elliptical nanoholes array (ENA) embedding through metal–dielectric–metal (MDM) multilayers. It is shown theoretically that a square periodic ENA perforating through MDM layers produces an FR response in the near infrared regime. This FR response is attributed to the interplay between the bright modes and dark modes, where the bright modes originate from the electric resonance (localized surface plasmon resonance) caused by the ENA and the dark modes are due to the magnetic resonance (inductive–capacitive resonance) induced by the MDM multilayers. Notably, one can achieve a narrower FR when the elliptical nanoholes occupy the sites of a rectangular lattice, owing to the interaction of the magnetic resonances with the enhanced electric resonances. Moreover, a higher varying degree of the lattice constant along the horizontal direction allows for an FR with a higher value of the quality factor and the tuning of the amplitude and the resonant frequency of the transparency window. Such an FR created by the interference among the magnetic and electric dipolar resonances opens up an alternative way of forming a sharp FR in the symmetric multilayer MMs, and could be exploited for sensing.
Design of Approximate Bilateral Filters for Image Denoising on FPGAs

This paper presents the hardware design of fast and low-cost denoising filters suitable to be exploited in the enabling technologies for Industry 5.0. A novel approximate computing strategy is introduced to reduce the computational complexity of the image denoising operation and to comply with real-time requirements. Firstly, it is demonstrated that the novel approximate approach can be helpfully exploited in the design of reconfigurable denoising filters able to reach image qualities as close as possible to the precise software counterparts. The reconfigurability leads to hardware architectures run-time adaptable to different levels of noise, whereas the adopted approximation strategy limits hardware resources and energy requirements. Quality tests, performed at various image and kernel sizes, and noise standard deviations, demonstrate that the approximate denoising approach presented here reaches PSNR and SSIM comparable with the precise denoise filtering. In comparison with state-of-the-art FPGA-based competitors, the novel filters reduce the resources requirements by up to 70%, achieve frame rates up to 35 times higher, and dissipate more than 45% lower power. When implemented within the XC7Z7020 FPGA device, a <inline-formula> <tex-math notation="LaTeX">$5\times 5$ </tex-math></inline-formula> filter designed as proposed here denoises <inline-formula> <tex-math notation="LaTeX">$512\times 512$ </tex-math></inline-formula> grayscale images using only 1689 LUTs, 2635 Flip-Flops and 32 DSPs. Moreover, it processes up to 926.8 frames per second, consumes just 63mW @ 244MHz and, with a noise standard deviation equal to 10, it achieves an average PSNR of <inline-formula> <tex-math notation="LaTeX">$\sim 33$ </tex-math></inline-formula>dB with an average SSIM of ~0.86.