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Free electrons in the interstellar medium refract and diffract radio waves along multiple paths, resulting in angular and temporal broadening of radio pulses that limits pulsar timing precision. We determine multifrequency, multi-epoch scattering times for the large dispersion measure millisecond pulsar J1903+0327 by developing a three component model for the emitted pulse shape that is convolved with a best fit pulse broadening function (PBF) identified from a family of thin-screen and extended-media PBFs. We show that the scattering time, $\tau$, at a fiducial frequency of 1500 MHz changes by approximately 10% over a 5.5yr span with a characteristic timescale of approximately 100 days. We also constrain the spectral index and inner scale of the wavenumber spectrum of electron density variations along this line of sight. We find that the scaling law for $\tau$ vs. radio frequency is strongly affected by any mismatch between the true and assumed PBF or between the true and assumed intrinsic pulse shape. We show using simulations that refraction is a plausible cause of the epoch dependence of $\tau$, manifesting as changes in the PBF shape and $1/e$ time scale. Finally, we discuss the implications of our scattering results on pulsar timing including time of arrival delays and dispersion measure misestimation., Comment: 16 pages, 14 figures, submitted to ApJ

The detection of a stochastic gravitational wave background by pulsar timing arrays suggests the presence of a supermassive black hole binary population. Although the observed spectrum generally aligns with predictions from orbital evolution driven by gravitational wave emission in circular orbits, there is a discernible preference for a turnover at the lowest observed frequencies. This turnover could indicate a significant hardening phase, transitioning from early environmental influences to later stages predominantly influenced by gravitational wave emission. In the vicinity of these binaries, the ejection of stars or dark matter particles through gravitational three-body slingshots efficiently extracts orbital energy, leading to a low-frequency turnover in the spectrum. By analyzing the NANOGrav 15-year data, we assess how the gravitational wave spectrum depends on the initial inner galactic profile prior to disruption by binary ejections, accounting for a range of initial binary eccentricities. Our findings suggest a parsec-scale galactic center density around $10^6\,M_\odot/\textrm{pc}^3$ across most of the parameter space, offering insights into the environmental effects on black hole evolution and combined matter density near galaxy centers., Comment: 15 pages, 5 figures

Time-of-arrival (TOA) measurements of pulses from pulsars are conventionally made by a template matching algorithm that compares a profile constructed by averaging a finite number of pulses to a long-term average pulse shape. However, the shapes of pulses can and do vary, leading to errors in TOA estimation. All pulsars show stochastic variations in shape, amplitude, and phase between successive pulses that only partially average out in averages of finitely many pulses. This jitter phenomenon will only become more problematic for timing precision as more sensitive telescopes are built. We describe techniques for characterizing jitter (and other shape variations) and demonstrate them with data from the Vela pulsar, PSR B0833$-$45. These include partial sum analyses; auto-and cross correlations between templates and profiles and between multifrequency arrival times; and principal component analysis. We then quantify how pulse shape changes affect TOA estimates using both analytical and simulation methods on pulse shapes of varying complexity (multiple components). These methods can provide the means for improving arrival time precision for many applications, including gravitational wave astronomy using pulsar timing arrays., Comment: 23 pages, 13 figures. Submitted to ApJ

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

Fast radio bursts (FRBs) are unique probes of extragalactic ionized baryonic structure as each signal, through its burst properties, holds information about the ionized matter it encounters along its sightline. FRB 20200723B is a burst with a scattering timescale of $\tau_\mathrm{400\,MHz} >$1 second at 400 MHz and a dispersion measure of DM $\sim$ 244 pc cm$^{-3}$. Observed across the entire CHIME/FRB frequency band, it is the single-component burst with the largest scattering timescale yet observed by CHIME/FRB. The combination of its high scattering timescale and relatively low dispersion measure present an uncommon opportunity to use FRB 20200723B to explore the properties of the cosmic web it traversed. With an $\sim$arcminute-scale localization region, we find the most likely host galaxy is NGC 4602 (with PATH probability $P(O|x)=0.985$), which resides $\sim$30 Mpc away within a sheet filamentary structure on the outskirts of the Virgo Cluster. We place an upper limit on the average free electron density of this filamentary structure of $\langle n_e \rangle < 4.6^{+9.6}_{-2.0} \times 10^{-5}$ cm$^{-3}$, broadly consistent with expectations from cosmological simulations. We investigate whether the source of scattering lies within the same galaxy as the FRB, or at a farther distance from an intervening structure along the line of sight. Comparing with Milky Way pulsar observations, we suggest the scattering may originate from within the host galaxy of FRB 20200723B., Comment: 20 pages, 6 figures, submitted. Comments welcome!

The identification of persistent radio sources (PRSs) coincident with two repeating fast radio bursts (FRBs) supports FRB theories requiring a compact central engine. However, deep non-detections in other cases highlight the diversity of repeating FRBs and their local environments. Here, we perform a systematic search for radio sources towards 37 CHIME/FRB repeaters using their arcminute localizations and a combination of archival surveys and targeted observations. Through multi-wavelength analysis of individual radio sources, we identify two (20181030A-S1 and 20190417A-S1) for which we disfavor an origin of either star formation or an active galactic nucleus in their host galaxies and thus consider them candidate PRSs. We do not find any associated PRSs for the majority of the repeating FRBs in our sample. For 8 FRB fields with Very Large Array imaging, we provide deep limits on the presence of PRSs that are 2--4 orders of magnitude fainter than the PRS associated with FRB\,20121102A. Using Very Large Array Sky Survey imaging of all 37 fields, we constrain the rate of luminous ($\gtrsim$10$^{40}$ erg s$^{-1}$) PRSs associated with repeating FRBs to be low. Within the context of FRB-PRS models, we find that 20181030A-S1 and 20190417A-S1 can be reasonably explained within the context of magnetar, hypernebulae, gamma-ray burst afterglow, or supernova ejecta models -- although we note that both sources follow the radio luminosity versus rotation measure relationship predicted in the nebula model framework. Future observations will be required to both further characterize and confirm the association of these PRS candidates with the FRBs., Comment: 37 pages, 12 figures

The rapid evolution of mobile networks from 5G to 6G has necessitated the development of autonomous network management systems, such as Zero-Touch Networks (ZTNs). However, the increased complexity and automation of these networks have also escalated cybersecurity risks. Existing Intrusion Detection Systems (IDSs) leveraging traditional Machine Learning (ML) techniques have shown effectiveness in mitigating these risks, but they often require extensive manual effort and expert knowledge. To address these challenges, this paper proposes an Automated Machine Learning (AutoML)-based autonomous IDS framework towards achieving autonomous cybersecurity for next-generation networks. To achieve autonomous intrusion detection, the proposed AutoML framework automates all critical procedures of the data analytics pipeline, including data pre-processing, feature engineering, model selection, hyperparameter tuning, and model ensemble. Specifically, it utilizes a Tabular Variational Auto-Encoder (TVAE) method for automated data balancing, tree-based ML models for automated feature selection and base model learning, Bayesian Optimization (BO) for hyperparameter optimization, and a novel Optimized Confidence-based Stacking Ensemble (OCSE) method for automated model ensemble. The proposed AutoML-based IDS was evaluated on two public benchmark network security datasets, CICIDS2017 and 5G-NIDD, and demonstrated improved performance compared to state-of-the-art cybersecurity methods. This research marks a significant step towards fully autonomous cybersecurity in next-generation networks, potentially revolutionizing network security applications., Comment: Accepted to the Workshop on Autonomous Cybersecurity, ACM CCS 2024; Code is available at Github link: https://github.com/Western-OC2-Lab/AutonomousCyber-AutoML-based-Autonomous-Intrusion-Detection-System

The NANOGrav 15-year data provides compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists in a simple power-law fit involving two parameters: an amplitude A and a spectral index \gamma. In this paper, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, \gamma_run(f) = \gamma + \beta \ln(f/f_ref). We fit this running-power-law (RPL) model to the NANOGrav 15-year data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95% credible interval for the parameter \beta consistent with no running, \beta \in [-0.80,2.96], and an inconclusive Bayes factor, B(RPL vs. CPL) = 0.69 +- 0.01. We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal; however, future data sets may well lead to a measurement of nonzero \beta. Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from big-bang nucleosynthesis, the cosmic microwave background, and LIGO-Virgo-KAGRA., Comment: 17 pages, 4 figures, 2 tables

In the rapidly evolving field of quantum computing, quantifying circuit complexity remains a critical challenge. This paper introduces Character Complexity, a novel measure that bridges Group-theoretic concepts with practical quantum computing concerns. By leveraging tools from representation theory, I prove several key properties of character complexity and establish a surprising connection to the classical simulability of quantum circuits. This new measure offers a fresh perspective on the complexity landscape of quantum algorithms, potentially reshaping our understanding of quantum-classical computational boundaries. I present innovative visualization methods for character complexity, providing intuitive insights into the structure of quantum circuits. The empirical results reveal intriguing scaling behaviors with respect to qubit and gate counts, opening new avenues for quantum algorithm design and optimization. This work not only contributes to the theoretical foundations of quantum complexity but also offers practical tools for the quantum computing community. As quantum hardware continues to advance, character complexity could play a crucial role in developing more efficient quantum algorithms and in exploring the fundamental limits of quantum computation.

Pulsar-timing-array experiments have reported evidence for a stochastic background of nanohertz gravitational waves consistent with the signal expected from a population of supermassive--black-hole binaries. Those analyses assume power-law spectra for intrinsic pulsar noise and for the background, as well as a Hellings--Downs cross-correlation pattern among the gravitational-wave--induced residuals across pulsars. These assumptions are idealizations that may not be realized in actuality. We test them in the NANOGrav 15 yr data set using Bayesian posterior predictive checks: after fitting our fiducial model to real data, we generate a population of simulated data-set replications, and use them to assess whether the optimal-statistic significance, inter-pulsar correlations, and spectral coefficients assume extreme values for the real data when compared to the replications. We confirm that the NANOGrav 15 yr data set is consistent with power-law and Hellings--Downs assumptions. We also evaluate the evidence for the stochastic background using posterior-predictive versions of the frequentist optimal statistic and of Bayesian model comparison, and find comparable significance (3.2\ $\sigma$ and 3\ $\sigma$ respectively) to what was previously reported for the standard statistics. We conclude with novel visualizations of the reconstructed gravitational waveforms that enter the residuals for each pulsar. Our analysis strengthens confidence in the identification and characterization of the gravitational-wave background as reported by NANOGrav., Comment: 20 pages, 14 Figures

Efficiently simulating quantum circuits on classical computers is a fundamental challenge in quantum computing. This paper presents a novel theoretical approach that achieves exponential speedups (polynomial runtime) over existing simulators for a wide class of quantum circuits. The technique leverages advanced group theory and symmetry considerations to map quantum circuits to equivalent forms amenable to efficient classical simulation. Several fundamental theorems are proven that establish the mathematical foundations of this approach, including a generalized Gottesman-Knill theorem. The potential of this method is demonstrated through theoretical analysis and preliminary benchmarks. This work contributes to the understanding of the boundary between classical and quantum computation, provides new tools for quantum circuit analysis and optimization, and opens up avenues for further research at the intersection of group theory and quantum computation. The findings may have implications for quantum algorithm design, error correction, and the development of more efficient quantum simulators.

Neutron stars and white dwarfs are both dense remnants of post-main-sequence stars. Pulsars, magnetars and strongly magnetised white dwarfs have all been seen to been observed to exhibit coherent, pulsed radio emission in relation to their rotational period. Recently, a new type of radio long period transient (LPT) has been discovered. The bright radio emission of LPTs resembles that of radio pulsars and magnetars. However, they pulse on timescales (minutes) much longer than previously seen. While minute timescales are common rotation periods for white dwarfs, LPTs are much brighter than the known pulsating white dwarfs, and dipolar radiation from isolated (as opposed to binary) magnetic white dwarfs has yet to be observed. Here, we report the discovery of a new $\sim$421~s LPT, CHIME J0630+25, using the CHIME/FRB and CHIME/Pulsar instruments. We used standard pulsar timing techniques and obtained a phase-coherent timing solution which yielded limits on the inferred magnetic field and characteristic age. CHIME J0630+25 is remarkably nearby ($170 \pm 80$~pc), making it the closest LPT discovered to date., Comment: Submitted

Autonomous Vehicles (AVs), furnished with sensors capable of capturing essential vehicle dynamics such as speed, acceleration, and precise location, possess the capacity to execute intelligent maneuvers, including lane changes, in anticipation of approaching roadblocks. Nevertheless, the sheer volume of sensory data and the processing necessary to derive informed decisions can often overwhelm the vehicles, rendering them unable to handle the task independently. Consequently, a common approach in traffic scenarios involves transmitting the data to servers for processing, a practice that introduces challenges, particularly in situations demanding real-time processing. In response to this challenge, we present a novel DL-based semantic traffic control system that entrusts semantic encoding responsibilities to the vehicles themselves. This system processes driving decisions obtained from a Reinforcement Learning (RL) agent, streamlining the decision-making process. Specifically, our framework envisions scenarios where abrupt roadblocks materialize due to factors such as road maintenance, accidents, or vehicle repairs, necessitating vehicles to make determinations concerning lane-keeping or lane-changing actions to navigate past these obstacles. To formulate this scenario mathematically, we employ a Markov Decision Process (MDP) and harness the Deep Q Learning (DQN) algorithm to unearth viable solutions.

Fast radio bursts (FRBs) are micro-to-millisecond duration radio transients that originate mostly from extragalactic distances. The emission mechanism responsible for these high luminosity, short duration transients remains debated. The models are broadly grouped into two classes: physical processes that occur within close proximity to a central engine; and central engines that release energy which moves to large radial distances and subsequently interacts with surrounding media producing radio waves. The expected emission region sizes are notably different between these two types of models. FRB emission size constraints can therefore be used to distinguish between these competing models and inform on the physics responsible. Here we present the measurement of two mutually coherent scintillation scales in the frequency spectrum of FRB 20221022A: one originating from a scattering screen located within the Milky Way, and the second originating from a scattering screen located within its host galaxy or local environment. We use the scattering media as an astrophysical lens to constrain the size of the lateral emission region, $R_{\star\mathrm{obs}} \lesssim 3\times10^{4}$ km. We find that this is inconsistent with the expected emission sizes for the large radial distance models, and is more naturally explained with an emission process that operates within or just beyond the magnetosphere of a central compact object. Recently, FRB 20221022A was found to exhibit an S-shaped polarisation angle swing, supporting a magnetospheric emission process. The scintillation results presented in this work independently support this conclusion, while highlighting scintillation as a useful tool in our understanding of FRB emission physics and progenitors., Comment: Submitted, comments welcome

Vibrations of damaged bearings are manifested as modulations in the amplitude of the generated vibration signal, making envelope analysis an effective approach for discriminating between healthy and abnormal vibration patterns. Motivated by this, we introduce a low-complexity method for vibration-based condition monitoring (VBCM) of rolling bearings based on envelope analysis. In the proposed method, the instantaneous amplitude (envelope) and instantaneous frequency of the vibration signal are jointly utilized to facilitate three novel envelope representations: instantaneous amplitude-frequency mapping (IAFM), instantaneous amplitude-frequency correlation (IAFC), and instantaneous energy-frequency distribution (IEFD). Maintaining temporal information, these representations effectively capture energy-frequency variations that are unique to the condition of the bearing, thereby enabling the extraction of discriminative features with high sensitivity to variations in operational conditions. Accordingly, six new highly discriminative features are engineered from these representations, capturing and characterizing their shapes. The experimental results show outstanding performance in detecting and diagnosing various fault types, demonstrating the effectiveness of the proposed method in capturing unique variations in energy and frequency between healthy and faulty bearings. Moreover, the proposed method has moderate computational complexity, meeting the requirements of real-time applications. Further, the Python code of the proposed method is made public to support collaborative research efforts and ensure the reproducibility of the presented work

The mergers of double neutron stars (DNSs) systems are believed to drive the majority of short $\gamma$-ray bursts (SGRBs), while also serving as production sites of heavy r-process elements. Despite being key to i) confirming the nature of the extragalactic SGRBs, ii) addressing the poorly-understood r-process enrichment in the ultra-faint dwarf galaxies (UFDGs), and iii) probing the formation process of DNS systems, the space velocity distribution of DNSs is still poorly constrained due to the small number of DNSs with well-determined astrometry. In this work, we determine new proper motions and parallaxes of two Galactic DNSs -- PSR J0509+3801 and PSR J1930-1852, using the Very Long Baseline Array, and estimate the transverse velocities $v_\perp$ of all the 11 isolated Galactic DNSs having proper motion measurements in a consistent manner. Our correlation analysis reveals that the DNS $v_\perp$ is tentatively correlated with three parameters: spin period, orbital eccentricity, and companion mass. With the preliminary $v_\perp$ distribution, we obtain the following findings. Firstly, the refined $v_\perp$ distribution is confirmed to agree with the observed displacements of the localized SGRBs from their host galaxy birth sites. Secondly, we estimate that around 11% and 25% of DNSs remain gravitationally bound to UFDGs with escape velocities of 15$\mathrm{~km~s^{-1}}$ and 25$\mathrm{~km~s^{-1}}$, respectively. Hence, the retained DNSs might indeed be responsible for the r-process enrichment confirmed so far in a few UFDGs. Finally, we discuss how a future ensemble of astrometrically determined DNSs may probe the multimodality of the $v_\perp$ distribution., Comment: 17 pages, 3 figures, 4 tables, accepted for publication in ApJ

As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats. While traditional ML models can enhance cybersecurity, their high energy and resource demands limit their applications, leading to the emergence of Tiny Machine Learning (TinyML) as a more suitable solution for resource-constrained environments. TinyML is widely applied in areas such as smart homes, healthcare, and industrial automation. TinyML focuses on optimizing ML algorithms for small, low-power devices, enabling intelligent data processing directly on edge devices. This paper provides a comprehensive review of common challenges of TinyML techniques, such as power consumption, limited memory, and computational constraints; it also explores potential solutions to these challenges, such as energy harvesting, computational optimization techniques, and transfer learning for privacy preservation. On the other hand, this paper discusses TinyML's applications in advancing cybersecurity for Electric Vehicle Charging Infrastructures (EVCIs) as a representative use case. It presents an experimental case study that enhances cybersecurity in EVCI using TinyML, evaluated against traditional ML in terms of reduced delay and memory usage, with a slight trade-off in accuracy. Additionally, the study includes a practical setup using the ESP32 microcontroller in the PlatformIO environment, which provides a hands-on assessment of TinyML's application in cybersecurity for EVCI., Comment: Accepted and to appear in IEEE Access; Code is available at GitHub link: https://github.com/Western-OC2-Lab/TinyML_EVCI

Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. To this end, the work presented in this paper leverages signal processing and Machine Learning (ML) methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects during pre and post-flight operation. With the help of dimensionality reduction techniques, the Random Forest algorithm exhibited the best performance and detected defective rotor blades perfectly. Additionally, a comprehensive analysis of the impact of various feature subsets is presented to gain insight into the factors affecting the model's classification decision process., Comment: Submitted to IEEE GlobeCom 2024

Large language models (LLMs) are rapidly emerging in Artificial Intelligence (AI) applications, especially in the fields of natural language processing and generative AI. Not limited to text generation applications, these models inherently possess the opportunity to leverage prompt engineering, where the inputs of such models can be appropriately structured to articulate a model's purpose explicitly. A prominent example of this is intent-based networking, an emerging approach for automating and maintaining network operations and management. This paper presents semantic routing to achieve enhanced performance in LLM-assisted intent-based management and orchestration of 5G core networks. This work establishes an end-to-end intent extraction framework and presents a diverse dataset of sample user intents accompanied by a thorough analysis of the effects of encoders and quantization on overall system performance. The results show that using a semantic router improves the accuracy and efficiency of the LLM deployment compared to stand-alone LLMs with prompting architectures., Comment: Submitted to IEEE GlobeCom 2024

We use cyclic spectroscopy to perform high frequency-resolution analyses of multi-hour baseband Arecibo observations of the millisecond pulsar PSR B1937+21. This technique allows for the examination of scintillation features in far greater detail than is otherwise possible under most pulsar timing array observing setups. We measure scintillation bandwidths and timescales in each of eight subbands across a 200 MHz observing band in each observation. Through these measurements we obtain intra-epoch estimates of the frequency scalings for scintillation bandwidth and timescale.Thanks to our high frequency resolution and the narrow scintles of this pulsar, we resolve scintillation arcs in the secondary spectra due to the increased Nyquist limit, which would not have been resolved at the same observing frequency with a traditional filterbank spectrum using NANOGrav's current time and frequency resolutions, and the frequency-dependent evolution of scintillation arc features within individual observations. We observe the dimming of prominent arc features at higher frequencies, possibly due to a combination of decreasing flux density and the frequency dependence of the plasma refractive index of the interstellar medium. We also find agreement with arc curvature frequency dependence predicted by Stinebring et al. (2001) in some epochs. Thanks to the frequency resolution improvement provided by cyclic spectroscopy, these results show strong promise for future such analyses with millisecond pulsars, particularly for pulsar timing arrays, where such techniques can allow for detailed studies of the interstellar medium in highly scattered pulsars without sacrificing the timing resolution that is crucial to their gravitational wave detection efforts., Comment: Accepted to ApJ

The cosmic merger history of supermassive black hole binaries (SMBHBs) is expected to produce a low-frequency gravitational wave background (GWB). Here we investigate how signs of the discrete nature of this GWB can manifest in pulsar timing arrays through excursions from, and breaks in, the expected $f_{\mathrm{GW}}^{-2/3}$ power-law of the GWB strain spectrum. To do this, we create a semi-analytic SMBHB population model, fit to NANOGrav's 15 yr GWB amplitude, and with 1,000 realizations we study the populations' characteristic strain and residual spectra. Comparing our models to the NANOGrav 15 yr spectrum, we find two interesting excursions from the power-law. The first, at $2 \; \mathrm{nHz}$, is below our GWB realizations with $p$-value significance $p = 0.05$ to $0.06$ ($\approx 1.8 \sigma - 1.9 \sigma$). The second, at $16 \; \mathrm{nHz}$, is above our GWB realizations with $p = 0.04$ to $0.15$ ($\approx 1.4 \sigma - 2.1 \sigma$). We explore the properties of a loud SMBHB which could cause such an excursion. Our simulations also show that the expected number of SMBHBs decreases by three orders of magnitude, from $\sim 10^6$ to $\sim 10^3$, between $2\; \mathrm{nHz}$ and $20 \; \mathrm{nHz}$. This causes a break in the strain spectrum as the stochasticity of the background breaks down at $26^{+28}_{-19} \; \mathrm{nHz}$, consistent with predictions pre-dating GWB measurements. The diminished GWB signal from SMBHBs at frequencies above the $26$~nHz break opens a window for PTAs to detect continuous GWs from individual SMBHBs or GWs from the early universe., Comment: 10 pages, 8 figures, 1 appendix, submitted to ApJ

Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

Experimentation in practical, end-to-end (E2E) next-generation networks deployments is becoming increasingly prevalent and significant in the realm of modern networking and wireless communications research. The prevalence of fifth-generation technology (5G) testbeds and the emergence of developing networks systems, for the purposes of research and testing, focus on the capabilities and features of analytics, intelligence, and automated management using novel testbed designs and architectures, ranging from simple simulations and setups to complex networking systems; however, with the ever-demanding application requirements for modern and future networks, 5G-and-beyond (denoted as 5G+) testbed experimentation can be useful in assessing the creation of large-scale network infrastructures that are capable of supporting E2E virtualized mobile network services. To this end, this paper presents a functional, modular E2E 5G+ system, complete with the integration of a Radio Access Network (RAN) and handling the connection of User Equipment (UE) in real-world scenarios. As well, this paper assesses and evaluates the effectiveness of emulating full network functionalities and capabilities, including a complete description of user-plane data, from UE registrations to communications sequences, and leads to the presentation of a future outlook in powering new experimentation for 6G and next-generation networks., Comment: This paper was submitted to IEEE Transactions on Network and Service Management

Cyber-security attacks pose a significant threat to the operation of autonomous systems. Particularly impacted are the Heating, Ventilation, and Air Conditioning (HVAC) systems in smart buildings, which depend on data gathered by sensors and Machine Learning (ML) models using the captured data. As such, attacks that alter the readings of these sensors can severely affect the HVAC system operations impacting residents' comfort and energy reduction goals. Such attacks may induce changes in the online data distribution being fed to the ML models, violating the fundamental assumption of similarity in training and testing data distribution. This leads to a degradation in model prediction accuracy due to a phenomenon known as Concept Drift (CD) - the alteration in the relationship between input features and the target variable. Addressing CD requires identifying the source of drift to apply targeted mitigation strategies, a process termed drift explanation. This paper proposes a Feature Drift Explanation (FDE) module to identify the drifting features. FDE utilizes an Auto-encoder (AE) that reconstructs the activation of the first layer of the regression Deep Learning (DL) model and finds their latent representations. When a drift is detected, each feature of the drifting data is replaced by its representative counterpart from the training data. The Minkowski distance is then used to measure the divergence between the altered drifting data and the original training data. The results show that FDE successfully identifies 85.77 % of drifting features and showcases its utility in the DL adaptation method under the CD phenomenon. As a result, the FDE method is an effective strategy for identifying drifting features towards thwarting cyber-security attacks., Comment: 6 pages: Submitted to 2024 IWCMC Smart Energy Workshop

The hyper-parameter optimization (HPO) process is imperative for finding the best-performing Convolutional Neural Networks (CNNs). The automation process of HPO is characterized by its sizable computational footprint and its lack of transparency; both important factors in a resource-constrained Internet of Things (IoT) environment. In this paper, we address these problems by proposing a novel approach that combines transformer architecture and actor-critic Reinforcement Learning (RL) model, TRL-HPO, equipped with multi-headed attention that enables parallelization and progressive generation of layers. These assumptions are founded empirically by evaluating TRL-HPO on the MNIST dataset and comparing it with state-of-the-art approaches that build CNN models from scratch. The results show that TRL-HPO outperforms the classification results of these approaches by 6.8% within the same time frame, demonstrating the efficiency of TRL-HPO for the HPO process. The analysis of the results identifies the main culprit for performance degradation attributed to stacking fully connected layers. This paper identifies new avenues for improving RL-based HPO processes in resource-constrained environments., Comment: 7 pages, Submitted to IEEE Internet of Things Magazine

One major goal in fast radio burst science is to detect fast radio bursts (FRBs) over a wide field of view without sacrificing the angular resolution required to pinpoint them to their host galaxies. Wide-field detection and localization capabilities have already been demonstrated using connected-element interferometry; the CHIME/FRB Outriggers project will push this further using widefield cylindrical telescopes as widefield outriggers for very long baseline interferometry (VLBI). This paper describes an offline VLBI software correlator written in Python for the CHIME/FRB Outriggers project. It includes features well-suited to modern widefield instruments like multibeaming/multiple phase center correlation, pulse gating including coherent dedispersion, and a novel correlation algorithm based on the quadratic estimator formalism. This algorithm mitigates sensitivity loss which arises in instruments where the windowing and channelization is done outside the VLBI correlator at each station, which accounts for a 30 percent sensitivity drop away from the phase center. Our correlation algorithm recovers this sensitivity on both simulated and real data. As an end to end check of our software, we have written a preliminary pipeline for VLBI calibration and single-pulse localization, which we use in Lanman et al. (2024) to verify the astrometric accuracy of the CHIME/FRB Outriggers array., Comment: 24 pages, 7 figures. Comments, reference suggestions, and PRs welcome!

The development of next-generation networks is revolutionizing network operators' management and orchestration practices worldwide. The critical services supported by these networks require increasingly stringent performance requirements, especially when considering the aspect of network reliability. This increase in reliability, coupled with the mass generation and consumption of information stemming from the increasing complexity of the network and the integration of artificial intelligence agents, affects transport networks, which will be required to allow the feasibility of such services to materialize. To this end, traditional recovery schemes are inadequate to ensure the resilience requirements of next-generation critical services given the increasingly dynamic nature of the network. The work presented in this paper proposes a probabilistic and fault-tolerant robust traffic grooming model for OTN-over-DWDM networks. The model's parameterization gives network operators the ability to control the level of protection and reliability required to meet their quality of service and service level agreement guarantees. The results demonstrate that the robust solution can ensure fault tolerance even in the face of demand uncertainty without service disruptions and the need for reactive network maintenance., Comment: Presented at DRCN 2024

The integration of Machine Learning and Artificial Intelligence (ML/AI) into fifth-generation (5G) networks has made evident the limitations of network intelligence with ever-increasing, strenuous requirements for current and next-generation devices. This transition to ubiquitous intelligence demands high connectivity, synchronicity, and end-to-end communication between users and network operators, and will pave the way towards full network automation without human intervention. Intent-based networking is a key factor in the reduction of human actions, roles, and responsibilities while shifting towards novel extraction and interpretation of automated network management. This paper presents the development of a custom Large Language Model (LLM) for 5G and next-generation intent-based networking and provides insights into future LLM developments and integrations to realize end-to-end intent-based networking for fully automated network intelligence., Comment: Presented at DRCN 2024

Fast radio bursts (FRBs) are bright, millisecond-duration radio bursts of cosmic origin. There have been several dozen FRBs found to repeat. Among them, those precisely localized provide the best opportunity to probe their multi-wavelength counterparts, local environment, and host galaxy that would reveal their origins. Here we report our X-ray, ultraviolet (UV) and optical observations with the $Swift$ satellite that were performed simultaneously in the radio band with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) observations of the repeating FRB 20190520B, aiming at detection of possible multi-wavelength bursts in association with radio bursts and multi-wavelength counterpart of the persistent radio source (PRS). While a total of 10 radio bursts were detected by FAST at the same time of $Swift$ observations, we detected neither X-ray, UV or optical bursts in accompany of the radio bursts, nor persistent multi-wavelength counterpart of the PRS. We obtained the energy upper limits ($3\sigma$) on any multi-wavelength bursts as $5.03 \times 10^{47}$ erg in the hard X-ray band (15-150 keV), $7.98 \times 10^{45}$ erg in the soft X-ray band (0.3-10 keV), and $4.51 \times 10^{44}$ erg in the U band, respectively. The energy ratio between soft X-ray (0.3-10 keV) and radio emission of the bursts is constrained as $<6\times10^{7}$, and the ratio between optical (U band) and radio as $<1.19\times10^{6}$. The 3$\sigma$ luminosity upper limits at the position of PRS are 1.04$\times10^{47}$ (15-150 keV), 8.81$\times10^{42}$ (0.3-10 keV), 9.26$\times10^{42}$ (UVW1), and 2.54$\times10^{42}$ erg s$^{-1}$ (U), respectively. We show that the PRS is much more radio loud than representative pulsar wind nebulae, supernova remnants, extended jet of Galactic X-ray binaries and ultraluminous X-ray sources, suggestive of boosted radio emission of the PRS., Comment: 12 pages, 7 figures, submitted to ApJ

Fast radio bursts (FRBs) last for milliseconds and arrive at Earth from cosmological distances. While their origin(s) and emission mechanism(s) are presently unknown, their signals bear similarities with the much less luminous radio emission generated by pulsars within our Galaxy and several lines of evidence point toward neutron star origins. For pulsars, the linear polarisation position angle (PA) often exhibits evolution over the pulse phase that is interpreted within a geometric framework known as the rotating vector model (RVM). Here, we report on a fast radio burst, FRB 20221022A, detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) and localized to a nearby host galaxy ($\sim 65\; \rm{Mpc}$), MCG+14-02-011. This one-off FRB displays a $\sim 130$ degree rotation of its PA over its $\sim 2.5\; \rm{ms}$ burst duration, closely resembling the "S"-shaped PA evolution commonly seen from pulsars and some radio magnetars. The PA evolution disfavours emission models involving shocks far from the source and instead suggests magnetospheric origins for this source which places the emission region close to the FRB central engine, echoing similar conclusions drawn from tempo-polarimetric studies of some repeating sources. This FRB's PA evolution is remarkably well-described by the RVM and, although we cannot determine the inclination and magnetic obliquity due to the unknown period/duty cycle of the source, we can dismiss extremely short-period pulsars (e.g., recycled millisecond pulsars) as potential progenitors. RVM-fitting appears to favour a source occupying a unique position in the period/duty cycle phase space that implies tight opening angles for the beamed emission, significantly reducing burst energy requirements of the source.

Localizing fast radio bursts (FRBs) to their host galaxies is an essential step to better understanding their origins and using them as cosmic probes. The CHIME/FRB Outrigger program aims to add VLBI-localization capabilities to CHIME, such that FRBs may be localized to tens of milliarcsecond precision at the time of their discovery, more than sufficient for host galaxy identification. The first-built outrigger telescope is KKO, located 66 kilometers west of CHIME. Cross-correlating KKO with CHIME can achieve arcsecond-scale localization in right ascension while avoiding the worst effects of the ionosphere. This paper presents measurements of KKO's performance throughout its commissioning phase, as well as a summary of its design and function. We demonstrate KKO's capabilities as a standalone instrument by producing full-sky images, mapping the angular and frequency structure of the primary beam, and measuring feed positions. To demonstrate the localization capabilities of the CHIME -- KKO baseline, we collected five separate observations each for a set of twenty bright pulsars, and aimed to measure their positions to within 5~arcseconds. All of these pulses were successfully localized to within this specification. The next two outriggers are expected to be commissioned in 2024, and will enable subarcsecond localizations for approximately hundreds of FRBs each year., Comment: 41 pages, 17 figures

Pavement rutting poses a significant challenge in flexible pavements, necessitating costly asphalt resurfacing. To address this issue comprehensively, we propose an advanced Bayesian hierarchical framework of latent Gaussian models with spatial components. Our model provides a thorough diagnostic analysis, pinpointing areas exhibiting unexpectedly high rutting rates. Incorporating spatial and random components, and important explanatory variables like annual average daily traffic (traffic intensity), asphalt type, rut depth and lane width, our proposed models account for and estimate the influence of these variables on rutting. This approach not only quantifies uncertainties and discerns locations at the highest risk of requiring maintenance, but also uncover spatial dependencies in rutting (millimetre/year). We apply our models to a data set spanning eleven years (2010-2020). Our findings emphasise the systematic unexplained spatial rutting effect, where some of the rutting variability is accounted for by spatial components, asphalt type, in conjunction with traffic intensity, is also found to be the primary driver of rutting. Furthermore, the spatial dependencies uncovered reveal road sections experiencing more than 1 millimeter of rutting beyond annual expectations. This leads to a halving of the expected pavement lifespan in these areas. Our study offers valuable insights, presenting maps indicating expected rutting, and identifying locations with accelerated rutting rates, resulting in a reduction in pavement life expectancy of at least 10 years., Comment: 29 pages, 5 figures and 3 tables in main text, 2 tables and 4 figures in appendix 4 figures; and 3 tables in supplementary

We present the discovery of twelve thus far non-repeating fast radio burst (FRB) sources, detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope. These sources were selected from a database comprising of order $10^3$ CHIME/FRB full-array raw voltage data recordings, based on their exceptionally high brightness and complex morphology. Our study examines the time-frequency characteristics of these bursts, including drifting, microstructure, and periodicities. The events in this sample display a variety of unique drifting phenomenologies that deviate from the linear negative drifting phenomenon seen in many repeating FRBs, and motivate a possible new framework for classifying drifting archetypes. Additionally, we detect microstructure features of duration $\lesssim$ 50 $\mu s$ in seven events, with some as narrow as $\approx$ 7 $\mu s$. We find no evidence of significant periodicities. Furthermore, we report the polarization characteristics of seven events, including their polarization fractions and Faraday rotation measures (RMs). The observed $|\mathrm{RM}|$ values span a wide range of $17.24(2)$ - $328.06(2) \mathrm{~rad~m}^{-2}$, with linear polarization fractions between $0.340(1)$ - $0.946(3)$. The morphological properties of the bursts in our sample appear broadly consistent with predictions from both relativistic shock and magnetospheric models of FRB emission, as well as propagation through discrete ionized plasma structures. We address these models and discuss how they can be tested using our improved understanding of morphological archetypes., Comment: 29 pages, 14 figures, submitted to ApJ, comments appreciated

The Zero-touch network and Service Management (ZSM) framework represents an emerging paradigm in the management of the fifth-generation (5G) and Beyond (5G+) networks, offering automated self-management and self-healing capabilities to address the escalating complexity and the growing data volume of modern networks. ZSM frameworks leverage advanced technologies such as Machine Learning (ML) to enable intelligent decision-making and reduce human intervention. This paper presents a comprehensive survey of Zero-Touch Networks (ZTNs) within the ZSM framework, covering network optimization, traffic monitoring, energy efficiency, and security aspects of next-generational networks. The paper explores the challenges associated with ZSM, particularly those related to ML, which necessitate the need to explore diverse network automation solutions. In this context, the study investigates the application of Automated ML (AutoML) in ZTNs, to reduce network management costs and enhance performance. AutoML automates the selection and tuning process of a ML model for a given task. Specifically, the focus is on AutoML's ability to predict application throughput and autonomously adapt to data drift. Experimental results demonstrate the superiority of the proposed AutoML pipeline over traditional ML in terms of prediction accuracy. Integrating AutoML and ZSM concepts significantly reduces network configuration and management efforts, allowing operators to allocate more time and resources to other important tasks. The paper also provides a high-level 5G system architecture incorporating AutoML and ZSM concepts. This research highlights the potential of ZTNs and AutoML to revolutionize the management of 5G+ networks, enabling automated decision-making and empowering network operators to achieve higher efficiency, improved performance, and enhanced user experience., Comment: 115 pages, 12 figures, 15 tables, submitted to Computer Networks