Recently, metaverse-empowered wireless systems have gained significant interest in the research community because of the appealing features of self-sustainability and proactive learning. Self-sustainability allows a system to run with the least amount of assistance from network administrators, whereas proactive learning allows for the development of machine learning models prior to user requests. As a result, the idea of the metaverse in vehicular networks can be used to enable a variety of applications (e.g., infotainment and collision avoidance) for a massive number of autonomous vehicles. However, metaverseempowered vehicular networks are challenging to implement due to computing (i.e., at autonomous cars and network edges) and communication resource constraints. We present a novel framework for joint sensing, communication, and task offloading for vehicular networks empowered by the metaverse in order to address these issues. We formulate a problem to minimize a cost function that takes into account transmission latency, sensing, and transmission energy. Sensing interval, resource distribution, and task offloading are all optimized for minimizing the cost. We use convex optimization for the sensing problem while a decomposition-relaxation-based approach is used for joint resource allocation and task offloading. Finally, numerical results are provided to support the proposed scheme.

Cardiovascular diseases are the primary cause of death worldwide. At-home monitoring systems can be used to prevent them. For long-term monitoring, these systems require high detection accuracy and low power consumption. This paper introduces a new cardiac arrhythmia classification scheme that employs antidictionaries for identifying abnormal patterns in electrocardiograms. This system enables the training to be performed without data augmentation and a smaller dataset for training compared to existing literature. The proposed method is also compatible with an eventdriven implementation, that offers great potential for ultra-low power devices. The reported average detection accuracy reaches 97.97%. The system is demonstrated through simulations and implementation on an FPGA platform.

Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and in particular for users of cochlear implants (CIs). To improve their listening experience, auditory attention decoding (AAD) aims to decode the target speaker of a listener from electroencephalography (EEG), and then use this information to steer an auditory prosthesis towards this speech signal. In normal-hearing individuals, deep neural networks (DNNs) have been shown to improve AAD compared to simpler linear models. AAD has also been shown to be feasible in CI users using linear models, however, it has not yet been shown that DNNs can yield enhanced decoding accuracies for this patient group. Here we show that attention decoding in CI users can be significantly improved through the usage of a convolutional neural network (CNN). To this end, we first collected an EEG dataset on selective auditory attention from 25 CI users, and then implemented both a linear model as well as a CNN for attention decoding. We observed superior performance of the CNN across all considered decision window sizes, ranging from 1 s to 60 s. Boosted by a Support Vector Machine (SVM) as a trainable classifier, the CNN decoder achieved a maximal mean decoding accuracy of 74% at the population level for a decision window of 60 s duration. Our findings illustrate that the progress made in AAD among normal hearing participants, facilitated by the integration of DNNs, extends to cochlear implant (CI) users.

This paper presents a novel data-driven approach to nonlinear system control using a behavioral systems framework. A Dynamic Latent Variable Autoencoder (DLVAE) is proposed to project the nonlinear physical variable space onto a linear latent variable space. A data-predictive control approach is developed to control the physical process variables through the latent variables. Based on the behavioral systems theory, the proposed data-driven control framework does not require the knowledge on the causality of the latent variables. The stability of the controlled system is ensured by utilizing the concept of trajectory-based dissipativity. The robustness of this control approach is achieved by incorporating the Lipschitz bounds between the latent and physical variable under dissipativity conditions.

Cell culture assays play a vital role in various fields of biology. Conventional assay techniques like immunohistochemistry, immunofluorescence, and flow cytometry offer valuable insights into cell phenotype and behavior. However, each of these techniques requires labeling or staining, and this is a major drawback, specifically in applications that require compact and integrated analytical devices. To address this shortcoming, CMOS capacitance sensors capable of conducting label-free cell culture assays have been proposed. In this paper, we present a computational framework for further augmenting the capabilities of these capacitance sensors. In our framework, identification and classification of mitosis and migration are achieved by leveraging observations from measured capacitance time series data. Specifically, we engineered two time series features that enable discriminating cell behaviors at the single-cell level. Our feature representation achieves an area under curve (AUC) of 0.719 in the receiver operating characteristic (ROC) curve. Additionally, we show that our feature representation technique is applicable across arbitrary experiments, as validated by a leaveone-run-out test yielding an F-1 score of 0.803 and a G-Mean of 0.647.

Reconfigurable intelligent surface (RIS)-assisted generalized spatial modulation (GSM) systems have recently been regarded as prospective candidates for the upcoming 6G wireless networks. This is primarily because GSM scheme utilizes the indices of the active antennas to convey extra spatial symbols, and RIS can intelligently adjust the wireless propagation environment, leading to a significant improvement in both network coverage and energy efficiency. This paper explores the potential of integrating multiple distributed RISs into GSM scheme and proposes a norm-based RIS selection algorithm to aid the transmission between the transmitter and receiver by selecting a number of RISs with the largest channel norm values. Simulation results demonstrate that the proposed multi-RIS-assisted GSM system with norm-based RIS selection algorithm exhibits superior bit error rate (BER) performance compared to conventional GSM system and multi-RIS-assisted GSM system without RIS selection. Moreover, the proposed system achieves higher power efficiency compared to multi-RIS-assisted GSM system without the selection strategy.

A cascaded multi-intelligent reflecting surface (IRS)assisted signed quadrature spatial modulation (SQSM) system is proposed in this paper with intelligently integrating single and multiple IRS into the SQSM scheme. In this system, the signal is transmitted via the reflection of single or cascaded multiple IRSs. The proposed system models, however, benefit from the spatial multiplexing gain and the signal-to-noise ratio (SNR) gain of SQSM and IRSs, respectively. The bit error rate (BER) performance of the proposed systems is evaluated and compared to the performance of the conventional SQSM system without IRS for the sake of fair comparison. The simulation results show that a considerable performance gain is achieved by the proposed cascaded multi-IRS-assisted SQSM system over the conventional SQSM scheme, and this gain increases with increasing the number of cascaded IRSs and the number of their passive elements. The close match is also noticed between the analytical and simulated BER performances of the proposed systems with high SNR values.

Weather variations can significantly impact the performance of high-frequency wireless networks. Although much research has been conducted on these impacts, there has been limited focus specifically on Citizens Broadband Radio Service (CBRS) networks, which operate at 3.5 GHz. Analyzing these effects is crucial for CBRS-based private networks, where reliability and high performance are critical. This paper explores how different weather conditions-temperature, relative humidity, absolute humidity, rainfall, and snowfall-affect the signal strength in the CBRS spectrum. Through an extensive experimental campaign within a CBRS-based private LTE network in Buffalo, NY, we collected signal strength data from 32 data collection points over 21 months. Using weather data from Oikolab, our data-driven analyses indicate that relative humidity is a more reliable predictor of signal strength fluctuations compared to temperature and absolute humidity. We also investigate how various types of precipitation affect signal strength, with rainfall causing immediate decreases and snowfall leading to more prolonged impacts. These insights are vital for enhancing the understanding of weather impacts on the CBRS spectrum, a topic not extensively covered in the existing literature.

This paper presents a method for robust model predictive control (MPC) of linear parameter varying (LPV) systems considering control policies that are affine functions of the parameter, which is possible when only the 'A' and not the 'B' matrix depends on the uncertain parameter (LPV-A systems). This is less conservative than previous formulations in which the policy is restricted to perturbations on a feedback law, as it includes such policies as a special case. State and input constraints are handled efficiently by bounding predicted states in a sequence of polyhedra (i.e. tube MPC), that are parameterised by variables in the online optimisation. The resulting controller can be implemented by online solution of a single quadratic programming problem and can exploit rate bounds on the LPV parameters, which requires a pre-processing step at each iteration. Recursive feasibility and exponential stability are proven and the approach is compared to existing methods in numerical examples drawn from other publications, showing reduced conservatism and improved regions of attraction.

Magnetic Resonance Fingerprinting (MRF) is theoretically more efficient than steady-state quantitative MRI techniques because it exploits dynamic behavior to enhance differences in signals obtained from tissues with different relaxation parameters. In practice, MRF often struggles to deliver the predicted performance, requiring careful adjustment of sequence parameters such as flip-angles, repetition times and k-space sampling patterns. MRF sequences result in a highly undersampled dynamic image series; state-of-the-art methods now exploit a temporal low-rank (TLR) reconstruction approach to help resolve some of the resulting undersampling artifacts. While successful, the TLR reconstruction mixes signals across space and through time, obscuring how sampling might be optimized for best results. This work explores optimal sampling for TLR reconstruction of MRF. We examine conditioning of the reconstruction problem as a predictor of image quality, and propose an effective optimization algorithm for k-space sampling on regular grids. Based on this, we compare different sampling schemes in simulations and real phantom experiments. We explain how undersampling generates aliasing, enhances noise and errors, and demonstrate how parallel-imaging improves this. We also conclude that the final reconstruction quality depends on a combination of undersampling, the expected signal distribution and errors present in real scans, and point towards how these might be included in further optimization efforts.

Personally Identifiable Information (PII) is any content that is sensitive that needs to be treated as secure and private. When data pieces such as a person's name, address, Social Security number, phone number, email address, and so on may be used to identify a specific individual, they are deemed PII. As organizations grow, so does their volume of data. This makes identifying and protecting such sensitive resources at a scale quite complex. In this project, we demonstrate where and how PII can be discovered and how we developed a working prototype of a tool that can easily detect PII images using advanced artificial intelligence (AI) techniques such Optical Character Recognition (OCR) and image classification using Convolutional Neural Networks (CNN).

Transformer isolated, multi-output power supplies (MOPS) are useful as gate drivers auxiliary supply in multi switch conversion systems such as motor drive inverters. This work investigated the use of series resonant capacitors at the secondaries of the transformer to compensate the leakage inductance and explored the issues of cross regulation and parasitic oscillation at light load. Analytical, simulation and experimental studies, conducted in this work, showed that good overall performance can be obtained by the proposed choice of the components of the MOPS. Synopsis Transformer isolated, multi-output auxiliary power supplies (MOPS) are useful as gate drivers supply in multi switch conversion systems such as motor drive inverters. Systems based on WBG devices require low interwinding capacitance of the MOPS isolation transformers. This gives rise to large leakage inductance of the transformer which in turn results in poor load stability and cross regulation. This work investigated the use of series resonant capacitors at the secondaries of the transformer to compensate the leakage inductance and explored the issues of cross regulation and parasitic oscillation at light load. The effects of the non-idealities were investigated analytically, by simulation and experimentally. The results of these studies were used to develop recommendations for considerably improving the performance of the MOPS by proper choice of the resonant capacitors, the rectifying diodes and an LDO which dissipate rather low power.

Shape sensing in continuum robotics enables stable actuation and control, as estimation of complex curvatures is essential for manoeuvring through complex environments, for applications in the manufacturing, aerospace, and medical industries, as well as space and rescue operations. This paper demonstrates the performance of an optoelectronic based shape sensing system integrated into a two-segment tendon actuated robotic manipulator. The sensing principle is proximity-intensity based sensing and utilises a convex shaped reflector for modulation of proximity during rotation in two degrees of freedom. For improved sensing performance, the shape sensing system utilises a simplified circuit design with features such as power switching properties for elimination of signal interference effects and for significant reduction in consumption of power, as well as inclusion of low friction tubes along the tendon routing paths, for reduced friction during large bending motions. A new streamlined technique for the calibration of the sensors is demonstrated, and a validation of the shape sensing performance shows improved estimates of tip position and orientation as well as shape of the robotic structure.

The quality of antenna and radar cross section measurement chambers is strongly dependent on the absorption behavior of the installed wave absorbers. It is demonstrated that the behavior of the absorbers can be assessed and illustrated with excellent spatial resolution by powerful microwave imaging techniques. When the absorbers are subjected to an illumination source, the fast irregular antenna field transformation algorithm (FIAFTA) enables simultaneous reconstruction of equivalent sources for both the illuminating source and the scatterers from near-field measurements. This process effectively implements echo suppression, allowing for the isolation of weakly scattered fields, primarily caused by the wave absorbers. Building upon this foundation, the application of microwave imaging technology enables the visual representation and examination of the absorbers. Experimental measurement results are shown to validate the feasibility of the approach.

Unmanned Aerial Vehicles (UAVs) have emerged as crucial components in advancing Mobile Edge Computing (MEC), leveraging their proximity to edge nodes and scalable nature. This synergy holds significant promise within the Internet of Things (IoT) and Beyond 5G (B5G) domains. In this paper, we concentrate on optimizing the shrinking ratio, a Quality of Experience (QoE) metric, within large-scale IoT networks empowered by UAV-enhanced MEC MEC via joint optimizing task offloading, resource allocation and UAV trajectories. This joint optimization problem presents significant challenges due to the intertwined nature of multiuser computing mode selection and strong coupling between User Equipments (UEs) waiting time and UAV trajectory. To tackle these challenges, we formulate the problem as a Mixed Integer Nonlinear Programming (MINLP) problem and proposed an iterative algorithm named BTOU by decomposing the original problem into two subproblems using the Block Coordinate Descent (BCD) framework. For the task offloading and resource allocation sub-problem, we present two algorithms: one employs a low-complexity greedy game-theoretic approach suitable for a large number of UEs, while the other leverages the Penalty Successive Convex Approximation (PSCA) technique along with first-order Taylor expansion approximation to achieve high solution quality. For the UAV trajectory planning sub-problem, we transform it into a Miller-Tucker-Zemlin (MTZ) model and devise a solution strategy. Extensive simulation results validate the effectiveness of our proposed algorithm, showcasing rapid convergence and a notable improvement in QoE of over 10% compared to benchmark methods.

Due to the complexity and high financial costs involved in production processes, the steel industry can greatly benefit from the application of intelligent systems capable of carrying out automated activities. This research describes the process of creating a data-driven computer system, based on artificial neural networks, applied to the process of predicting temperatures in slab reheating furnaces. Recurrent Artificial Neural Networks have been widely studied and applied with the aim of making predictions based on past knowledge, through historical sequences that have temporal links, a typical case of monitoring industrial process variables. The research investigates the performance of predictive neural models, such as Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN). The work also explores data pre-processing and hyperparameter tuning to obtain accurate models. The metrics of mean absolute error (MAE) and root mean square error (RMSE) were used to measure prediction accuracy. The results were evaluated under different prediction horizons, since such techniques demand models capable of accurate predictions that are several steps ahead, premised on prediction capability.

Accurately imaging the spatial distribution of longitudinal sound speed has a profound impact on image quality and the diagnostic value of ultrasound. Knowledge of sound speed distribution allows effective aberration correction to improve the image quality. Sound speed imaging also provides a new mechanism of imaging contrast that will facilitate disease diagnosis. However, sound speed imaging is challenging in the pulse-echo mode. Deep learning (DL) is a promising approach for pulse-echo speed of sound imaging, which may yield more accurate results than pure physics-based approaches. However, it often requires a large amount of training data and has limitations in model generalizability. To address these issues, we developed a physics-guided DL approach to image the spatial distribution of sound speed using pulse-echo data acquired from multiple angles. Instead of using the raw echo signals as the input to the DL model, we transformed the echo signals into time-shift maps based on local phase shifts between beamformed images. Using this approach, our model was shown to be generalizable when trained and tested using different scan settings in simulation studies. Furthermore, the simulation-trained model successfully reconstructed the sound speed maps of phantoms using experimental data. Compared with a state-of-the-art physics-based inversion approach, our approach reduced median root mean squared error (RMSE) by 26% and improved contrast-to-noise ratio (CNR) by 52% in the phantom experiment. These results demonstrated the accuracy and robustness of our approach.

Millimeter wave tilted beam horn antennas (mmWave) hold immense potential for reducing atmospheric absorption and multi-path effects that hinder signal quality at these frequencies. However, conventional design techniques employ bulky structures, have complex geometries, don't utilize low-cost air-filled SIW technology (AFSIW), suffer from poor bandwidth, and exhibit comparatively poor gain and low elevation scanning coverage. To address these concerns, an AFSIW horn antenna with a tilted beam is proposed for the first time. The design exhibits a large bandwidth over the entire V-Band, a superior gain of 15.51 dBi, and a large elevation scanning coverage. Furthermore, the work also proposes an AFSIW multibeam antenna array utilizing these tilted-beam AFSIW horn antennas for the first time. This array finds application in urban-air-mobility (UAM), and satellite communication systems, where mitigating multi-path effects is crucial for enhancing signal quality. State of art performance is observed when the work is compared to reported advances in the literature. In conclusion, the work addresses a critical gap in tilted-beam antenna design but also paves the way for cost-effective, highperformance solutions for mmWave communication.