Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning-based mmWave Path Loss Prediction for Urban/Suburban Macro Sites

Millimeter-Wave (mmWave) has great potential to provide high data dates given its large available bandwidth, but its severe path loss and high propagation sensitivity to different environmental conditions make deployment planning particularly challenging. Traditional slope-intercept models fall short in capturing large site-specific variations due to urban clutter, terrain tilt or foliage, and ray-tracing faces challenges in characterizing mmWave propagation accurately with reasonable complexity. In this work, we apply machine learning (ML) techniques to predict mmWave path loss on a link-to-link basis over an extensive set of 28 GHz field measurements collected in a major city of USA, with over 120,000 links from both urban and suburban scenarios, with over 40 dB variation for links at similar distances. Either raw environmental profile (terrain+clutter) of each link or 8 selected expert features are used to either directly predict path loss via regression-based approaches or predict the best performing option out of a pool of theoretical/empirical propagation models. Our evaluation shows that Lasso regression provides the best path loss prediction with a performance (RMSE 8.1 dB) comparable to the per-site slope-intercept fit (RMSE 8.0 dB), whereas model selection method achieves 8.6 dB RMSE, both are significantly better than the best a posteriori 3GPP model (UMa-NLOS, 10.0 dB).
Decentralized adaptive stabilization of infinite networks of switched nonlinear systems with unknown control directions

We solve the problem of decentralized adaptive stabilization for infinite networks of hierarchically interconnected switched systems in strict-feedback form with unknown switching signals and with unknown control directions. We assume that each node is interconnected with only finite set of its neighbors and the number of neighbors of each node has a uniform upper boundary. The trajectories of the closed-loop system with our switching-independent adaptive feedback law converge to the origin with respect to the ℓ∞-norm, and this property holds uniformly with respect to the unknown switching signals.
FMS:Features Motion Statistics for Incorrect Matched-pair Removal

The matching of incorrect pairs affects the precision of the SLAM/VO system. Because the calculation is complex and time-consuming, the various limitation methods struggle to meet the system's real-time requirements. FMS is a novel approach that adapts well to both sparse and dense mapping processes, allowing for the verification of matched pairs of features. By displacing features between two consecutive frames, the projection formula contacts motion patterns. This classifier's overall characteristics demonstrated that it was extremely effective at applying suitable matched pairings from mismatched features. The proposed algorithm outperformed the raw technique in terms of accuracy and stability when compared to the ORB-SLAM technique.
Integrating Aerial Base Stations for sustainable urban mobile networks

The extensive densification of mobile networks is increasing the network energy consumption and leading to remarkable economical and sustainability concerns. At the same time, regulatory and physical constraints, especially in urban environments, may limit the network expansion and the free installation of Base Stations (BSs). In this context, High Altitude Platform Stations (HAPSs) are emerging as a promising solution to host aerial BSs that can provide additional capacity over a wide geographical area, to offload the on-ground mobile network and support a sustainable transition towards the 6G era. This paper investigates the potential of HAPS offloading to reduce the energy demand from the grid and the operational cost of mobile networks. Our results highlight the effectiveness of HAPS offloading in reducing the size of the RE supply that is required to achieve grid energy reduction on the terrestrial network, thus enhancing the feasibility of a sustainable evolution towards 6G networks. Different allocation strategies are designed and analyzed under several configuration settings, to dynamically adapt the HAPS capacity to the traffic variability in space and over time. A fine tuning of the strategy settings is proved effective in trading off physical constraints, operational cost, sustainability goals, and Quality of Service.
Global exponential set-point regulation for linear operator semigroups with input saturation

In this paper the problem of set-point regulation of linear operator semigroups is considered, in presence of saturation of the control input. It is shown how global exponential stability can still be obtained by adding an anti-windup loop to the integral action. As first contribution towards the solution of this regulation problem, we give some new results related to the characterization of global exponential stability of the equilibrium of a subclass of semi-linear dynamical systems.
RangeSRN: Range Super-Resolution Network Using mmWave FMCW Radar

Designing a signal-processing algorithm for frequency-modulated continuous-wave (FMCW) radar applications with advanced functionality remains a challenging problem. Specifically, traditional algorithms improve detection resolution by increasing bandwidth and contribute to inefficient spectral use and maximum range reduction. To strike a balance between the maximum detection range and range resolution, we propose a resolution improvement algorithm based on super-resolution techniques. The low-resolution detection results were used to infer the high-resolution data by emphasizing the hidden spatial correlations. Simulation results confirmed that the proposed algorithm possesses an outstanding ability to achieve high-resolution detection while employing reduced bandwidth, leading to two advantages: spectral efficiency and maximum detectable range.
Atomic Force Microscope Vertical Feedback Control Strategy for Semi-Automated Long-Range Probe Landing

Traditional Atomic Force Microscopes (AFM) allow a short range displacement of the AFM probe, on the order of several tens of micrometers. When used inside an Electron Microscope (EM), the probe must be able to move on a millimeter scale with nanometer resolution. This is essential for the probe to reach any region of interest on a sample observed by an EM. In this paper, we address a challenging issue related to semi-automated long-range landing of an AFM probe on a sample. The probe is mounted on a piezoelectric inertial actuator. It is initially several millimeters away from the sample and must be safely landed to a distance on the order of hundred of nanometers (intermittent contact region). The control strategy is divided into three steps: (i) long-range velocity control using a stepping control, (ii) short and fine position control using a mixed stepping/scanning control, and (iii) position/force control using a scanning control. While traditional manual landing methods take a tremendous amount of time to complete the procedure, the proposed semi-automated method enables a safe long-range landing in less than 3 minutes. More generally, this is the first experimental demonstration in the literature of such a capability in AFM.
Passivity-Based Finite-Time Consensus for Nonlinear Fractional-Order Multi-Agent Systems

This paper utilizes the finite-time passivity (FTP) to settle the finite-time consensus (FTC) problem for a class of nonlinear fractional-order multi-agent systems (FOMASs). By selecting appropriate state feedback controller, a FTP criterion for FOMAS is derived on the basis of inequality techniques, and a sufficient condition to ensure FOMAS can achieve FTC is also given by exploiting the FTP result. Moreover, to testify the validity of the FTP and FTC criteria, a numerical example is presented.
Learning-Based Detection of Malicious Hosts by Analyzing Non-Existent DNS Responses

DNS Water Torture attack is a type of DDoS attack on authoritative DNS servers and/or open resolvers, whereby the victim is bombarded with random non-existent domains (NXDs) DNS requests, exhausting their entire resources. A famous example of this attack was launched by Mirai botnet on Dyn DNS architecture in 2016. Researchers have proposed solutions to detect these attacks; however, they predominantly apply static thresholds to the count of NXD responses. This method can result in high false positives and needs to be customized to the traffic pattern of victim DNS servers, making it practically challenging for adoption at the source of potential attacks. This paper aims to detect possibly infected hosts of a university campus network that take part in this specific type of DNS-based attacks. Our contributions are threefold: (1) We analyze 120 days' worth of DNS traffic collected from the border of a large university campus network to draw insights into the characteristics of non-existent domain (NXD) responses from incoming DNS packets. We discuss how malicious NXDs differ from benign ones and highlight two attack scenarios based on their requested domain names; (2) We develop a method using multi-staged iForest models to detect malicious internal hosts based on the attributes of their DNS activity; (3) We evaluate the efficacy of our proposed method by applying it to live DNS data streams in our university campus network. We show how our models can detect infected hosts that generate high-volume and low-volume distributed non-existent DNS queries with more than 99% accuracy of correctly classifying legitimate hosts.
Exogenous Disturbance Estimation for Autonomous Navigation around Small Celestial Bodies

In this article, we propose the implementation of a Nonlinear Moving Horizon Estimation (NMHE) framework to estimate exogenous disturbances acting on a spacecraft for autonomous navigation around Small Celestial Bodies (SCBs). The estimation framework is coupled with a Nonlinear Model Predictive Control (NMPC) to promote robust autonomous operations in Space. The NMHE based exogenous disturbance estimation formulates a finite horizon optimization problem, while incorporating the lumped disturbances as an additional augmented state vector. Next, the estimated disturbance is utilized by the NMPC controller in a feed-forward manner. Numerous closed-loop simulations have been conducted to assess the validity of the proposed estimation and disturbance rejection framework by considering: a) Inertial hovering around two different asteroid bodies namely 433 Eros and Ryugu and b) unavailability of two primary accurate asteroid characterization data, the asteroid rotation rate na and the gravitational parameter µ. The presented results are compared with an Extended Kalman Filter (EKF) and NMPC formulation to validate the efficacy of the proposed framework.
Data-Adaptive Retrofit Control for Power System Stabilizer Design

In this paper, we propose a design procedure of data-adaptive power system stabilizers (PSSs) in the framework of retrofit control. The proposed procedure is modular in the sense that both design and implementation processes of PSSs can be performed using only a local subsystem model and local measurement. In particular, we consider online identification of a dynamical feedback effect between the states of a generator of interest and the main grid to make the PSS adaptive to the variation of grid characteristics depending on power flow distributions. The main theoretical contribution is to show that the same retrofit controller as that developed for linear systems in the literature works properly even for nonlinear power systems where an operating point of interest varies depending on power flow distributions. In addition, we propose an online identification algorithm of the grid characteristics that makes use of the physical structure of power systems. We demonstrate the efficacy of the proposed data-adaptive PSS by a numerical simulation on the IEEE 9-bus test power system.
Learning Graph Convolutional Networks Based on Quantum Vertex Information Propagation

This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. Unlike state-of-the-art Graph Convolutional Neural Network (GCNN) models, the proposed QSGCNN model incorporates the process of identifying transitive aligned vertices between graphs and transforms arbitrary sized graphs into fixed-sized aligned vertex grid structures. In order to learn representative graph characteristics, a new quantum spatial graph convolution is proposed and employed to extract multi-scale vertex features, in terms of quantum information propagation between grid vertices of each graph. Since the quantum spatial convolution preserves the grid structures of the input vertices (i.e., the convolution layer does not alter the original spatial position of vertices), the proposed QSGCNN model allows to directly employ the traditional convolutional neural network architecture to further learn from the global graph topology, providing an end-to-end deep learning architecture that integrates the graph representation and learning in the quantum spatial graph convolution layer and the traditional convolutional layer for graph classifications. We indicate the effectiveness of the proposed QSGCNN model in relation to existing state-of-the-art methods. Experiments on benchmark graph classification datasets demonstrate the effectiveness of the proposed QSGCNN model.
Contactless Blood Pressure Monitoring with mmWave Radar

The monitoring of blood pressure is critical for the prevention, diagnosis and treatment of cardiovascular diseases. However, existing methods require physical contact between human body and sensor, which are not suitable for long-term monitoring. In this paper, we propose a contactless blood pressure monitoring system, mmBP, using millimeter wave radar. Specifically, we first separate signals reflected from different spatial locations by coherently combining the signals on different antennas. Then, we locate and extract the arterial pulse using convolutional neural network (CNN) assisted template matching with location tracking. Finally, we design an encoder-decoder neural network to derive the blood pressure information from the extracted signal. Experimental results on 20 subjects show that the measurement deviation rate is 9.00% and 3.69% for systolic and diastolic blood pressure, which demonstrates the feasibility and effectiveness of the proposed system.
Room-by-Room Device Grouping for Put-and-Play IoT System

In this study, we propose a Put-and-Play (PnP) Internet of Things (IoT) system, an IoT system that requires no initial setup. IoT systems require the initial setup consisting of device location information setup, network configurations, and device coordination. Although configuration automation and assistant methods for network configurations and device coor-dination have been proposed, device location information setup still needs manual operations. This paper therefore proposes a room-by-room device grouping method that groups IoT devices in the same room. We utilize IEEE 802.11ac Channel State Information (CSI) to group IoT devices in the same room with a non-supervised learning algorithm. Experimental evaluations conducted in a smart house environment reveal that our device grouping method successfully groups IoT devices in the same room with an adjusted Rand index (ARI) of up to 1.00.
Resilient Scheduling of Control Software Updates in Power Distribution Systems

In response to newly found security vulnerabilities, or as part of a moving target defense, a fast and safe control software update scheme for networked control systems is highly desirable. We here develop such a scheme for intelligent electronic devices (IEDs) in power distribution systems, which is a solution to the so-called software update rollout problem. This problem seeks to minimize the makespan of the software rollout, while at the same time guaranteeing acceptable voltage and current levels at all buses despite possible worst-case update failure where malfunctioning IEDs may inject harmful amount of power into the system. Utilizing the nonlinear DistFlow equations, we can rewrite the voltage and current conditions as a set of load and network dependent linear inequalities with respect to the rollout schedule with quantifiable degree of conservatism. Assuming update failure detectors such as out-of-range voltage relays, the optimal software rollout schedule can be time-slotted so that the rollout problem can be reformulated as a variant of the classical combinatorial bin packing problem. Demonstration with a realistic distribution system benchmark is provided to verify the practical significance of our work.
Categorical Diversity-Aware Inner Product Search

The problem of maximum inner product search (MIPS) is one of the most important components in machine learning systems. However, this problem does not care about diversity, although result diversification can improve user satisfaction. This paper hence considers a new problem, namely the categorical diversity-aware IPS problem, in which users can select preferable categories. Exactly solving this problem needs <inline-formula> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> time, where <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of vectors, and is not efficient for large <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. We hence propose an approximation algorithm that has a probabilistic success guarantee and runs in sub-linear time to <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. We conduct extensive experiments on real datasets, and the results demonstrate the superior performance of our algorithm to that of a baseline using an existing MIPS technique.
Distribution Network Congestion Management with Internet Data Centers Using Bi-level Programming

Large-scale demand-side resources (DSRs) like geo-distributed internet data centers (IDCs) access to the distribution network may cause security problems, e.g. line congestion. The demand response (DR) method is wildly used according to price signals to motivate users to schedule the loads. This paper proposes a bi-level programming optimization framework for distribution network congestion management considering power network and workload allocation. The upper level is the workload scheduling strategy of an internet service company (ISC) with multiple IDCs to minimize the total electric power cost. The lower level is the strategy of distributional locational marginal pricing (DLMP) based on direct current optimal power flow (DCOPF) to minimize the total generating cost. Three scenarios with a modified IEEE-30 bus system are studied and analyzed to prove the effectiveness of the proposed strategy both in cost reduction and congestion management.
Machine Learning-based Approaches Comparison for Netfiix/DAZN Streaming and Real Traffic Prediction

Traffic prediction is a fundamental topic in modern networks for performance analysis and network planning. To this aim, modeling and optimization of modern heterogeneous network infrastructures are key factors to achieve such goals, e.g. in terms of traffic flow improvement while reducing bandwidth allocation. Identifying an accurate model of a network device (e.g., a switch or a router) behavior is crucial in order to apply advanced and optimal resource allocation techniques. Such a problem is very challenging due to nonlinearities and unavailability of internal variables measurements in real devices. In this respect, a promising direction is given by an appropriate integration of System Identification and Machine Learning (ML) techniques to obtain predictive models using historical data collected from the network. In this paper, a comparison of various ML-based methodologies to learn accurate models of the dynamical input-output behavior of a network switch device, obtained by appropriately combining AutoRegressive (AR) model identification with Regression Trees (RTs) and Random Forests (RFs), is provided. Furthermore, a validation over a real dataset obtained from measurements of an Italian Internet Service Provider (Sonicatel S.r.1.) is provided, as well as a comparison with classical and widely used ML methods.
Discrete-Time Output Feedback under Bounded Actuators: Single and Multi-agent Problems

Synthesis results are presented for a general discrete-time dynamic output feedback controller and its anti-windup augmentation for operation under bounded actuation. The approach is then adopted for the leader-follower tracking problem for a multi-agent system. We consider the l2 gain from the disturbances to a general output, and obtain upper bounds for such gains. The scheme developed includes sufficient conditions, in the form of LMIs, for both controller design and anti-windup synthesis. Numerical examples are presented to show the effectiveness.
DNN-based anomaly prediction for the uncertainty in visual SLAM

The method described in this paper proposes a supervised Deep Neural Network (DNN) approach for the prediction of anomalies in camera-based navigation. The method is inspired by the unsolved issues of Integrity Monitors (IMs) when some of the sensor measurement covariances are unknown or inconsistent. Especially, the focus is on predicting when the estimation error distribution would require fatter tails to include outliers. The developed method takes into account single-frame image features as well as transient changes in the error. In the best of our knowledge, this is the first work that predicts anomalies in the error covariance of Simultaneous Navigation and Mapping (SLAM) estimates and associates them with low-level image features. Finally, the prediction method can be used with other sensors as well, allowing the future development of navigation algorithm- and sensor-agnostic safety monitoring frameworks.
Primal-Dual Estimator Learning Method with Feasibility and Near-Optimality Guarantees

This paper proposes a primal-dual framework to learn a stable estimator for linear constrained estimation problems leveraging the moving horizon approach. To avoid the online computational burden in most existing methods, we learn a parameterized function offline to approximate the primal estimate. Meanwhile, a dual estimator is trained to check the suboptimality of the primal estimator during execution time. Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality. This in turn allows us to bound the probability of the learned estimator being infeasible or suboptimal. Furthermore, we analyze the stability of the resulting estimator with a bounded error in the minimization of the cost function. Since our algorithm does not require the solution of an optimization problem during runtime, state estimates can be generated online almost instantly. Simulation results are presented to show the accuracy and time efficiency of the proposed framework compared to online optimization of moving horizon estimation and Kalman filter. To the best of our knowledge, this is the first learning-based state estimator with feasibility and near- optimality guarantees for linear constrained systems.
Moving Horizon Estimation with Adaptive Regularization for Ill-Posed State and Parameter Estimation Problems

We investigate the usage of Moving Horizon Estimation (MHE) for state and parameter estimation for partially non-detectable systems with measurements corrupted by outliers. We propose an arrival cost update formula based on the Generalized Gauss-Newton method and illustrate how it can be generalized to nonconvex loss functions that can be effectively used for outlier rejection. Moreover, we propose an adaptive regularization scheme for the arrival cost which introduces forgetting as well as additional pseudo-measurements to the arrival cost update. We illustrate the performance of the proposed algorithms on a longitudinal vehicle state and parameter estimation problem.