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Automated Log Message Embeddings

System logs are crucial for understanding the state and health of systems, yet manual inspection becomes impractical due to the high volume of messages. Consequently, machine learning-based log anomaly detection has emerged to automatically identify irregularities. This study investigates the effectiveness of log message embeddings, a novel parsing method, for anomaly detection in complex systems.

Radiation Efficiency and Gain Bounds for Microstrip Patch Antennas

This paper presents bounds on radiation efficiency and gain for microstrip patch antennas, demonstrating close alignment with the performance of classic antenna designs. These bounds serve as effective benchmarks for assessing antenna performance and evaluating trade-offs and design feasibility. The study particularly addresses the trade-off between miniaturization and performance by comparing bou

Navigating the Challenges and Opportunities of Securing Internet of Autonomous Vehicles With Lightweight Authentication

The Internet of Things (IoT) can be defined as the network of physical objects, or "things,"embedded with sensors and software for processing and exchanging data with other devices and ecosystems using the Internet as a medium. With its rapid growth over the past decade, it has permeated several application domains, including intelligent vehicular systems. The Internet of Autonomous Vehicles (IoAV

A Novel Decentralized Leader–follower Control Scheme for Centroid and Formation Tracking

This paper deals with the centroid and formation control problem of multi–agent robotic systems. The proposed solution is based on a leader–follower scheme, where only a subset of agents, i.e., the leaders, knows the desired trajectories for the centroid and the formation of the system, while the other agents, i.e., the followers, are required to estimate them through a dynamic consensus scheme. T

Fading in Reflective and Heavily Shadowed Industrial Environments With Large Antenna Arrays

One of the required communication solutions to support novel use cases, e.g. in industrial environments, for 5G systems and beyond is ultra-reliability low-latency communication (URLLC). An enabling technology for URLLC is massive multiple-input multiple-output (MIMO), which with its large antenna arrays can increase reliability due to improved user separation, array gain and the channel hardening

Analysis of Control Systems Under Sensor Timing Misalignments

This paper presents an in-depth analysis of the stability and performance of control systems experiencing sensor timing misalignments, a common challenge in practical applications such as autonomous vehicles and aerospace systems. We model multichannel sensor delays as independent random variables, capturing the variability of real-world systems where different sensors exhibit distinct and non-con

Channel Orthogonalization With Reconfigurable Surfaces : General Models, Theoretical Limits, and Effective Configuration

We envision a future in which multi-antenna technology effectively exploits the spatial domain as a set of non-interfering orthogonal resources, allowing for flexible resource allocation and efficient modulation/demodulation. We may refer to this paradigm as orthogonal space-division multiplexing (OSDM). On the other hand, reconfigurable intelligent surface (RIS) has emerged as a promising technol

Storms, ships, and shovels : A trans-Holocene history of the Gullåkra wetland

The Gullåkra wetland is located around 5 km south of the modern city of Lund and around 2.7 km east of the important Iron Age settlement of Uppåkra in Scania, southern Sweden. In the 1840s, a remarkable discovery was made in the wetland: a bronze lur dated to c. 1300 BCE, along with a boat and the bones of a large animal offering. As Bronze Age boats are exceedingly rare, this discovery makes the

Replication package for code review as decision-making

Code review is a well-established and valued practice in the software engineering community contributing to both code quality and interpersonal benefits. However, there are challenges in both tools and processes that give rise to misalignments and frustrations. Recent research seeks to address this by automating code review entirely, but we believe that this risks losing the majority of the interp

ALogSCAN: A Self-Supervised Dual Network for Adaptive and Timely Log Anomaly Detection in Clouds

Logs are prevalent in modern cloud systems and serve as a valuable source of information for system maintenance. Over the years, many supervised, semi-supervised, and unsupervised log analysis methods have been proposed to detect system anomalies. In particular, semi-supervised methods have garnered increasing attention as they balance reduced labeled data requirements and optimal detection perfor

Channel Orthogonalization in Panel-Based LIS

Large intelligent surface (LIS) has gained momentum as a potential 6G-enabling technology that expands the benefits of massive multiple-input multiple-output (MIMO). On the other hand, orthogonal space-division multiplexing (OSDM) may give a promising direction for efficient exploitation of the spatial resources, analogous as what is achieved with orthogonal frequency-division multiplexing (OFDM)

Adaptive Control of Positive Systems with Application to Learning SSP

An adaptive controller is proposed and analyzed for the class of infinite-horizon optimal control problems in positive linear systems presented in (Ohlin et al., 2024b). This controller is derived from the solution of a “data-driven algebraic equation” constructed using the model-free Bellman equation from Q-learning. The equation is driven by data correlation matrices that do not scale with the n

A new approach to relay-based autotuning PID controllers and their evaluation in pH control of industrial photobioreactors

This paper presents a novel adaptive autotuning strategy for controlling the pH in microalgae raceway reactors, a critical variable in optimizing biomass productivity under dynamic environmental conditions. The method expands on existing relay-based autotuning principles, tailored to suit the seasonally variable dynamics of microalgae cultivation systems influenced by CO2 injection and photosynthe

Rat Paw Tracking for Detailed Motion Analysis

This work is part of a research project studying the learning of fine motor skills in rats. A system for tracking a rat paw using a system of high-speed cameras is presented along with preliminary analysis of the correlation between paw movement and neural data. The tracking method is generative, modeling the rat paw as a set of linked ellipsoids. To find the most probable paw pose a number of hyp

High-Resolution Channel Sounding and Parameter Estimation in Multi-Site Cellular Networks

Understanding of electromagnetic propagation properties in real environments is necessary for efficient design and deployment of cellular systems. In this paper, we show a method to estimate high-resolution channel parameters with a massive antenna array in real network deployments. An antenna array mounted on a vehicle is used to receive downlink long-term evolution (LTE) reference signals from n

Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information

Opportunistic navigation using cellular signals is appealing for scenarios where other navigation technologies face challenges. In this paper, long-term evolution (LTE) downlink signals from two neighboring commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the received sig

Propagation Modeling for Physically Large Arrays: Measurements and Multipath Component Visibility

This paper deals with propagation and channel modeling for physically large arrays. The focus lies on acquiring a spatially consistent model, which is essential, especially for positioning and sensing applications. Ultra-wideband, synthetic array measurement data have been acquired with large positioning devices to support this research. We present a modified multipath channel model that accounts

EzSkiROS: A Case Study on Embedded Robotics DSLs to Catch Bugs Early

When we develop general-purpose robot software components, we rarely know the full context that they will execute in. This limits our ability to make predictions, including our ability to detect program bugs early. Since running a robot is an expensive task, finding errors at runtime can prolong the debugging loop or even cause safety hazards. In this paper, we propose an approach to help develope

Dual Control by Reinforcement Learning Using Deep Hyperstate Transition Models

In dual control, the manipulated variables are used to both regulate the system and identify unknown parameters. The joint probability distribution of the system state and the parameters is known as the hyperstate. The paper proposes a method to perform dual control using a deep reinforcement learning algorithm in combination with a neural network model trained to represent hyperstate transitions.

Enhanced Effective Aperture Distribution Function for Characterizing Large-Scale Antenna Arrays

Accurate characterization of large-scale antenna arrays is growing in importance and complexity for the fifth-generation (5G) and beyond systems, as they feature more antenna elements and require increased overall performance. The full 3D patterns of all antenna elements in the array need to be characterized because they are in general different due to construction inaccuracy, coupling, antenna ar