Given the sensors’ path and interference mitigation capabilities, Ultra-Wideband (UWB) based positioning systems have demonstrated high accuracy and reliability. This work aims to improve the Sage-Husa Fuzzy Adaptive filter (SHFAF) proposed in previous works by modifying the motion model to a 3D groundbased differential drive robot using IMU and wheel encoder kinematic fused control inputs. In addition to the changed motion model kinematics, this paper improved the positive definite constraint on P and R during dynamic estimations, thus making the filter more robust to outliers. An improvement to the computation and derivation of the fuzzy logic system for the SHFAF based on the ANFIS structure was developed, and training the fuzzy system using gradient descent was applied to improve the system’s accuracy. Experimental validation was conducted using real-world data from a Clearpath Jackal robot equipped with Qorvo UWB sensors and static nodes. In terms of localization accuracy, the proposed VelSHFAF system outperformed the previous SHFAF implementation by approximately 30% and 25% across two test courses, demonstrating its enhanced performance and reliability.

A document by Zohaib Akhtar. Click on the document to view its contents.

As wireless technology advances toward the sixth generation (6G) communication systems, ambient backscatter communication (AmBC) has emerged as a key enabler for lowcost internet of things (IoT) networks. This paper introduces a novel joint localization and communication (JLAC) technique based on orthogonal time sequency multiplexing (OTSM), specifically designed for passive backscatter devices (BDs). The proposed design enhances spectral efficiency by supporting broadband direct link communication, reduces synchronization requirements by leveraging the spreading characteristics of OTSM carriers, and ensures reliable BC even in the presence of strong direct link interference (DLI) without the need for successive interference cancellation (SIC). The approach utilizes pilot carriers that are protected by a guard in the delay-sequency (DS) domain, to achieve diversity gains along the delay domain while ensuring zero interference from either the data or the direct link. The scheme also exploits the pilot signal for localization, using received signal strength (RSSI) from BDs to localize the transmitter. Furthermore, the scheme minimizes the energy consumption at the BDs by reducing the switching rate (SR) by a factor of M. Extensive analysis and simulations demonstrate the proposed OTSM-based JLAC system's robustness to DLI, synchronization mismatches, power efficiency, and low complexity characteristics.

Sparse hyperspectral unmixing is a popular technique used for earth observation. Nonconvex sparse prior is often considered and the linearized ADMM is practically used to solve the problems. This note provides a detailed analysis that supports a recent consequence and experimental results dedicated to this class of algorithms [1]. We prove the convergence of the linearized ADMM for separable reweighted sparse hyperspectral unmxing but the extended algorithm and the proposition also cover relaxed scenarios where the variables are non-separable.

We show that changes in transmittance, in TFBG hydrogen sensors coated with nanometric metal/metal-hydride coatings with highly lossy cladding modes, are primarily a function of the mode losses, which we demonstrate, can be well estimated from the coating reflectivity. Using this result, and a simplified set of coupled mode equations that depend only on the mode losses, the sensor transmittance is recovered. We use this computationally fast model to show how the change in resonance amplitudes with hydrogen loading, and the envelope of resonant peaks, is tuned via an multi-layer metal/metal-hydride coating.

This study examines the fundamental theory necessary for comprehending Variational Autoencoders (VAEs) and generative latent time-series models. We will also explain how these models extend the principles of VAEs to the domain of time-series data by incorporating temporal dependencies into the latent space. By leveraging the probabilistic nature of VAEs and the temporal dependencies captured by generative latent time-series models—researchers and practitioners can generate synthetic data for various applications, ranging from image generation to time-series forecasting. Through experiments and examples, we showcase the efficacy of these models in generating synthetic data that closely resembles the characteristics of the original dataset.

Microscopic images of cells often contain multiple objects with similar properties. In this study, we collected videos of blood microcirculation in nailfold capillaries to identify correlations between video data and indicators of complete blood count tests. The specific features of these videos include a high number of capillaries and a high variance in their visual quality. Therefore, we proposed a novel loss function and a video processing framework that combines metric learning and classification with uncertainty estimation. We also applied test-time augmentation to enhance the confidence of the predictions. During simulation experiments, we used shallow and deep learning models to compare a set of classifiers for the collected metadata. Both types of models demonstrated good results, with an accuracy of up to 80% in gender classification. Deep learning models outperformed shallow models and also classified the age of the volunteers with an accuracy of 74%. Subsequently, we addressed a series of binary classification tasks to identify conditionally high or low levels of the main complete blood count indicators. The obtained accuracy of the models reached 63%. Although the accuracy of the blood test classifiers is not yet sufficient for clinical purposes, we have demonstrated that video data correlates with some of the indicators.

Network slicing, a cornerstone technology for future networks, enables the creation of customized virtual networks on a shared physical infrastructure. This fosters innovation and agility by providing dedicated resources tailored to specific applications. However, current orchestration and management approaches face limitations in handling the complexity of new service demands within multi-administrative domain environments. This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems, offering a framework that can be integrated with existing Management and Orchestration (MANO) frameworks. This framework leverages LLMs to translate user intent into technical requirements, map network functions to infrastructure, and manage the entire slice lifecycle, while multi-agent systems facilitate collaboration across different administrative domains. We also discuss the challenges associated with implementing this framework and potential solutions to mitigate them.

Public-key Encryption with Keyword Search (PEKS) is a promising cryptographic mechanism that enables a semi-trusted cloud server to perform (on-demand) keyword searches over encrypted data for data users. Existing PEKS schemes are limited to precise or fuzzy keyword searches, creating a gap given the widespread use of wildcards for rapid searches in real-world applications. To address this issue, several wildcard keyword search schemes have been proposed to support wildcard searches in the public key setting. However, these schemes suffer from inefficiency and/or inflexibility. Worse yet, they are all vulnerable to (insider) keyword guessing attacks (KGA), which is highly effective when the keyword space is polynomial in size. To address these vulnerabilities, this paper first proposes a new wildcard keyword search scheme called Public-key Encryption with Wildcard Search (PEWS), which is built based on the standard Decisional Diffie-Hellman (DDH) assumption. The complexity of all algorithms in PEWS increases linearly with the number of keywords, while remaining almost constant or even decreasing linearly with the number of wildcards. To resist against (insider) KGA, we further extend PEWS into the first Public-key Authenticated Encryption with Wildcard Search (PAEWS) scheme. Our PEWS and PAEWS schemes are highly flexible, supporting searches not only for sets of keywords but also for any number of wildcards positioned anywhere within the keyword set. We conduct a comprehensive performance evaluation of our PEWS and PAEWS, while also comparing PEWS with the state-of-the-art scheme in the public key setting. The experimental results demonstrate that both PEWS and PAEWS are efficient and practical in computation and communication, and the experimental comparisons illustrate that PEWS achieves significant improvements in computational efficiency.

Intelligent agents are transforming industries by enhancing efficiency, personalization, and decisionmaking. This paper explores the applications of reactive, proactive, hybrid, and learning agents across healthcare, finance, manufacturing, retail, transportation, and education. Through examples and case studies, we highlight the impact of agents and their potential to drive innovation and optimize operations.

This paper explores the cost analysis of vehicle recalls between 2010 and August 2024, focusing on Ford, Tesla, and Toyota. The study highlights Tesla's significantly lower recall volumes and costs compared to traditional automakers, despite rapid growth in the electric vehicle market. This research investigates the importance of integrating software-based solutions and vertical manufacturing processes to reduce recall costs. The approach involved analyzing recall data from the National Highway Traffic Safety Administration (NHTSA). The analysis is primarily based on categories such as air bags, electrical systems, and powertrain issues, considering other major concerns for each automaker and their financial implications. The results show that Tesla's reliance on software updates and fewer mechanical components results in fewer recalls, lower logistics costs, and more efficient issue resolution. In contrast, Ford and Toyota face higher recall volumes due to mechanical complexity. The study concludes that Tesla's innovative approach to recalls sets a benchmark for cost-effective recall management, offering traditional automakers a blueprint for reducing recall costs and improving operational efficiency.

Pedicle screw placement (PSP) is a challenging procedure in spine surgery, due to poor visualization of the vertebrae and blood vessels. This has led to the development of navigation systems that improve safety and clinical outcomes. Navigation further enabled minimal invasive approaches for PSP. Current navigation systems require a preoperative computed tomography (CT) scan, followed by manual planning by the surgeon to decide screw trajectories. Intraoperative fluoroscopy imaging may be employed to register the preoperative plan with the patient's anatomy frame or even design screw trajectory planning in real-time for each pedicle individually. This is a rather lengthy procedure that involves harmful radiation to which the surgeon is exposed repeatedly. This article explores a novel approach for PSP guidance that replaces manual planning while solely relying on 3D ultrasound (US) reconstruction, hence reducing the need for radiation. Based on an intraoperative US reconstruction, a set of anatomical landmarks of the spine are identified. Subsequently, a coordinate frame is computed per vertebra. The screw path is then generated automatically using the learned relations between the landmarks and optimal screw paths. A five-fold cross-validation is conducted on the posterior surface of CT spine data involving 90 PSP paths. The algorithm is then tested on another 50 PSP paths of the 3D-printed spine phantoms corresponding to human spine CT data. Results show that for 4 mm and 6 mm diameter screws, 98% and 84% of the computed trajectories are within the required surgical precision, respectively. Using the US-based path planning led to-0.34 ± 3.66 • and-0.45 ± 4.32 • orientation errors in the sagittal and axial planes, respectively.

The surgical procedure or Deep Brain Stimulation (DBS) is an established technique aimed at neuromodulation and is most often used for treating neurological and neuropsychiatric disorders. In clinical studies, Magnetoencephalography (MEG) is a widely used technique for characterizing the effects of Deep Brain Stimulation (DBS). A significant limitation of DBS stimulation is the inability to distinguish stimulation artifacts from actual neuronal activity, especially in the lower frequencies, where it may obscure the biological response and be a confounding factor. This paper aims to understand how to circumvent the DBS artifactual component in the summary metrics of DBS-corrupted MEG data. To do the same, we used a watermelon as a phantom model of the head to deploy DBS electrodes. The spectral signature of the artifactual component of DBS and it's power in different clinically significant bands of interest were analyzed in the frequency domain. We present the preliminary results for the same in this paper.

Vertical conductive structure (VeCS) technology, an advanced interconnect technology for signal transition on different printed circuit board (PCB) layers, was developed to meet needs for more intelligent PCB technologies with decreased layer count, and improved signal integrity and power integrity with respect to those of conventional transition via structures. In contrast to the conventional via structure, the VeCS consists of a plate signal trace and an oval-shaped shielding ground racket comprising a semi-coaxial structure or a vertical transmission line. The VeCS achieves a continuous characteristic impedance that improves electrical performance regarding losses, crosstalk, and electromagnetic interference. Herein, the physical structure of the VeCS is first introduced. Subsequently, the time domain reflectometer impedance of the VeCS is extracted and analyzed through full-wave simulations. The crosstalk between VeCS-based and via-based channels on the PCB is compared to clarify the advantages of VeCS technology. Finally, measurements are used to further demonstrate the advantages of VeCS technology.

Optimal current setpoints in the form of Maximum torque per ampere (MTPA) and Field Weakening (FW) curves are well-known for three-phase machines. Fivephase machines have more freedom in searching for optimum since the current waveform contains except the first harmonic also the third harmonic, which has a role in torque enhancement and waveform shaping. The problem does not allow for an analytical solution and has to be solved numerically. In this contribution, we analyze the resulting solution and provide guidance for its implementation. Specifically, we show that the optimal operation has MTPA and FW regimes that have an inner structure with multiple sub-regimes. This variety comes from the limits imposed on the maximum of the waveforms of the phase voltage and phase current (peak shaving). A taxonomy of these regimes is provided, and their mutual relations are demonstrated on two exemplary machines. Moreover, we show that commonly used assumptions on the symmetry of the waveforms yield suboptimal results. The theory is validated experimentally on a laboratory prototype of a fivephase PMSM drive of 3 kW.

The proposal presented in this paper focuses on the process of classification of electrical faults in a transformer environment. Although the application is very specific in a very concrete field, the scope of the research has a wider scope since it is a multivariate time series classification system. It is based on two already known proposals such as the InceptionTime and Gated Transformer models, in which an attempt is made to extract the benefits provided by each of them. From the first one we extract its optimal computation time, and from the second one the way in which it handles multivariability. Although in the latter case, better results are obtained by modifying and changing the gating proposal for a linear double layer in its output, and the elimination of one of the bottleneck layers in charge of the relationships between the different time variables used. The study focuses on a potential future practical application in which processing time is prioritized together with a high percentage of classification accuracy. Finally, a comparative study is presented between the four models studied or proposed on a proprietary database created for the development of this and future work.

Much has been written about the representation of noisy linear 2-ports. Here we present a theory of noisy Nports. We show how in the general case there are (2N)!/(N !) 2 equivalent representations and give the transformations relating them. We also discuss singular cases in which some of the transformations are not possible as well as how to measure the noise properties of an N-port.

Kurdish handwritten character recognition (KHCR) is an emerging field, which deals with the complications brought forth in recognizing Kurdish handwritten text due to the peculiar characteristics of various scripts and dialects of Kurdish. A review on the status of KHCR was discussed here; important challenges in recognition were listed as data scarcity, intricacies in cursive writing, and variability in the individual's handwriting style. The existing works within this area have been involved in the development of neural network models and datasets; however, there are still a lot of limitations concerning the completeness of available datasets and the precision of the recognition system. The future directions in KHCR research put weight on developing largescale and diverse datasets, including all forms of Kurdish letters, using advanced techniques in deep learning, and realizing realtime recognition systems. This research has come up with a hybrid model combining the traditional approaches of feature extraction with the modern methods of machine learning, including user adaptation in order to improve the accuracy, and dialectal variability of Kurdish handwriting-all these will be very important in the future. By focusing on these areas, KHCR can significantly enhance digital communication and accessibility for Kurdish speakers while facilitating the preservation and digitalization of the Kurdish language. This continuous research in the domain carries the potential to create an inclusive digital environment that will also enable further expansion in use and application related to Kurdish handwriting recognition technologies.