The fifth-generation new radio coding techniques, such as low-density parity-check (LDPC) codes, have made significant progress. However, their limitations in the low error floor region prevent them from meeting the stringent bit error rate (BER) targets required for sixth-generation (6G) communications. 6G aims to support services with BER as low as 10 −9 , providing the reliability needed for mission-critical applications. his is particularly crucial for ultra-reliable lowlatency communication. In this paper, we present a two-stage decoding method that combines the strengths of layered LDPC decoding and federated transfer learning (FTL). The layered LDPC decoding stage effectively handles the challenging waterfall region, reducing errors during the initial decoding process. In addition, the FTL model adapts to mitigate the error floor and accelerate its decline. This combination achieves lower error rates and faster performance improvements, reducing the need for retransmissions and enhancing overall system efficiency.

The era of Quantum is approaching faster than expected due to recent advancements in the field. As it comes closer to reality, data encryption becomes a major issue. The need for quantum-resistant cryptography algorithms has increased. The National Institute of Standards and Technology (NIST) under the United States Department of Commerce has issued a set of standards. Algorithms that pass each of these stages are approved and are called Post Quantum Cryptography (PQC) Algorithms [1]. Currently at the fourth round of its standardization process, NIST has approved four algorithms. These algorithms have been widely implemented on software platforms, but their hardware acceleration still needs a lot of work. Latest developments in the field of FPGAs aids seamless implementation and promise a hardwaresoftware co-design. This article explores the implementation of NIST-approved PQC algorithms on the AMD PYNQ-Z2 Board using Python [2]. PYNQ Z2 is AMD's open-source project that provides us a platform to integrate programs in Python with an FPGA. Stress tests have been performed on this design and its metrics have been stated. [3] The Future scope of PQC algorithms in an FPGA has been summarized.

Intelligent job matching technology utilizes artificial intelligence to help people find suitable jobs more effectively. However, the development of this technology also brings a series of ethical and privacy issues, with data security and algorithm fairness being two main concerns. This paper synthesizes existing research, first introducing the background and current status of intelligent job matching technology, and then focusing on the challenges and solutions in data security and algorithm fairness, supported by relevant literature. Finally, through case analysis and future outlook, the development trends of ethical and privacy considerations in intelligent job matching technology are discussed.

We present a novel variant of the transformer encoder architecture designed to enhance predictive ability through sequential bottom-up reasoning with cross-resolution communication. The architecture incorporates cross-scale attention to enable interactions between different hierarchical levels of resolution, auxiliary scale-specific predictors to guide each transformer encoder layer to adopt a distinct resolution in the respective output embedding space, and a variational autoencoder operating on the input embeddings to establish a continuous latent space that facilitates sampling and analysis of how perturbations propagate throughout the network across different levels of resolution.

Deep unfolding (DU) methods constitute a promising class of neural networks that have shown good performance in image reconstruction and denoising for compressive hyperspectral imaging. Existing DU methods, however, have difficulties in balancing inference speed and reconstruction accuracy due to their limitations in the information transmission capacity, degradation estimation method, denoiser structure and training strategy. This paper proposes a novel DU architecture, dubbed degradation-estimated hybrid unfolding transformer network (DHUTNet), to improve the reconstruction quality and inference speed simultaneously. Firstly, a novel degradation-estimated hybrid unfolding framework (DHUF) is proposed to improve the generalization ability and training efficiency. The recovery module in DHUF includes a linear projection branch and a deep recovery branch. Moreover, the deep recovery branch estimates a pixel-specific degradation pattern from the projection error in each stage. Secondly, a dense-prior fusion module is designed to connect different stages of DHUF to further improve the reconstruction quality with less computation burden. Thirdly, a Ushape hybrid-attention transformer is introduced in the denoising module of DHUTNet to efficiently model various correlations of hyperspectral images. Finally, a novel progressive inherited training strategy for the larger-version DHUTNet is proposed, which significantly reduces the training time and improves the reconstruction quality. Experimental results demonstrate that the proposed DHUTNet outperforms existing methods, and can improve the inference speed and training efficiency up to 5-fold and 21-fold, but still achieve equivalent reconstruction quality.

In today's data-driven world, understanding and managing vast amounts of information is crucial for success in various fields. This research paper outlines the significance of data management systems, focusing on Big Data Infrastructure Plans and Information System Design. We will explore different data elements, the importance of designing Big Data infrastructure, and the practical application of data models. Furthermore, the paper discusses the difference between traditional Database Management Systems (DBMS) and Big Data Management Systems (BDMS), the handling of streaming data, and the rationale behind the diversity of data management systems.

The integration of computational techniques in language processing, often referred to as Natural Language Processing (NLP), has transformed the way humans interact with machines. This paper explores the critical role of computation in language understanding and generation. It addresses the strategic considerations, methodologies, tools, and challenges involved in NLP. Practical use cases demonstrate its significance across various industries, and dependencies highlight the need for synergistic technologies. This research outlines activation steps, risks, and tools essential for implementing computation-driven language solutions, providing a comprehensive guide for both researchers and practitioners.

Because of the particularity of the diagnosis task, the software system is required to correctly identify all possible fault types, especially to prevent misdiagnosis or missing diagnosis of abnormal faults. It is also required to evaluate the severity of the fault and predict the deterioration trend to assist users to make timely and correct responses. After the fault diagnosis task is completed, a decision is made as to whether maintenance is required. If maintenance is required, comprehensive analysis and evaluation of feasible maintenance technical schemes are carried out to select the most satisfactory maintenance schemes.

High-density surface EMG (HD-sEMG) is gaining attention because of its non-invasive nature and high spatial resolution. However, wearable HD-sEMG measurements with over 128 channels face challenges in system integration and reliable network-free inter-device synchronization. This article presents a wireless distributed wearable HD-sEMG acquisition system named Oct-HD, which supports up to eight acquisition modules (512 channels) with microsecond-level synchronization without requiring a network. The full-channel impedance detection ensures reliable electrode-skin contact and signal acquisition. The system also includes a self-locking base station that stores, charges, and configures the modules. Synchronization validation shows a longterm inter-module offline error within 2 ms (0.139 ppm) under vibrations and extreme temperatures, demonstrating reliability for network-free outdoor and open-space monitoring. Comparative experiments with a commercial system (Sessantaquattro) involving ten hand postures and 17 subjects were conducted, as hand gesture recognition is one of the most common applications in the field of electromyography. Results showed a significant performance improvement (p < 10 −5) of Oct-HD over the state-of-theart system in signal quality and anti-interference capacity. Gesture classification accuracy increased significantly for both the dynamic task (p = 0.008) and the maintenance task (p = 0.003). This highlights the effectiveness of Oct-HD in enhancing applications in prosthetic control and human-computer interaction. The Oct-HD system offers a notable advancement in wireless HD-sEMG acquisition, offering superior channel capacity, synchronization precision, signal quality, and anti-interference capacity compared to existing systems. The network-free microsecond-level synchronization and enhanced signal performance provide greater monitoring flexibility across various muscle groups, paving the way for broader applications in human-machine interaction.

This paper explores sign language, a natural mode of communication for the deaf community. However, sign language often remains challenging to learn and creates communication barriers between the deaf and the hearing. This work addresses this issue by assessing the performance of the state-of-the-art convolutional model, ConvNeXt, on the novel task of sign language recognition. The research yields compelling results, with accuracies surpassing 99% and fast training times that rival advanced Vision Transformers (ViTs). The experiments are rigorously evaluated using the publicly available Sign-Language-MNIST Dataset, an established benchmark for sign language research. A comparison of the generalizability of ConvNeXt and ViT is further undertaken using the publicly available Indian Sign Language Dataset which shows ViTs generalize better by ~3% in sign language recognition tasks. The findings of this study contribute to the broader goal of improving communication for the deaf community while also highlighting the capability of carefully constructed lightweight convolutional models that have recently fallen out of favour.

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3D near-field (NF) passive radar imaging approach for complex environments is presented. It utilizes frequency domain inverse source solutions and spatial image generation by coherent superposition of the automatically phase corrected single-frequency images. 3D near-field (NF) passive radar imaging approach for complex environments is presented. It utilizes frequency domain inverse source solutions and spatial image generation by coherent superposition of the automatically phase corrected single-frequency images.A The fields scattered from the imaging scene together with the fields radiated from the illuminating transmitter (Tx) are captured through NF measurements. The single-frequency inverse source solutions reconstruct simultaneously equivalent surface sources for the (Tx), the targets of interest (TOIs), and possibly present echo scatterers, which are subsequently separated due to its spatial localization. Spectral representations are computed for the Tx and TOI sources and single-frequency 3D images are obtained by hierarchical disaggregation. Finally, the single-frequency images are coherently superimposed utilizing an appropriate phase correction methodology. The phase correction is derived from the reconstructed Tx sources and compensates for the spatially varying phase shifts between the Tx and the scatterers, as well as for delays caused by the measurement instrumentation. Both numerical simulations and experimental measurement results are shown to validate the feasibility of the approach.

Spinal diseases such as spinal degeneration and scoliosis might require pedicle screw placement (PSP) as a crucial step during surgical interventions, depending on the severity. This procedure requires the drilling of a hole for placing the screw. Thanks to imaging modalities such as computed tomography and intraoperative fluoroscopy, moving from an open approach to minimally invasive surgery (MIS) has been possible and has reduced patient complications after surgery. Still, it suffers from a lack of visual feedback for the surgeon, whereas robotic-assisted spine surgery combined with such imaging modalities can improve surgical outcomes for PSP. Yet, in such MIS procedures, physical motion, such as breathing motion, can induce shifts and deformations in the spine, leading to operation errors of approximately 2-3 mm [1]. In order to correctly identify the entry point, breathing motion needs to be compensated for. External sensors, such as an optical tracking system or range imaging, can be used to measure the motion of the skin or neighboring vertebrae, close to the entry point [2], [3]. However, Saghbiny et al. showed that the amplitude of breathing motion changes over vertebrae and has a variation of 68% from the lumbar to thoracic vertebrae [4]. Therefore, this work develops an approach for estimating the motion of each entry point, enhancing the accuracy. The proposed method uses a long short-term memory network (LSTM) on top of the inner control loop to estimate breathing motion parameters for each pedicle drilling individually, and its output is utilized to update the motion model for motion compensation during robot-assisted drilling for MIS-PSP.

Interpersonal synchrony (IS), a crucial indicator of social interactions, is traditionally studied through video data and manual coding methods. Our study aims to develop automated coding and visualization tools for time series data collected by IMU sensors, offering a more efficient, objective, and scalable analysis of interaction dynamics. This study evaluates several time series similarity analyses, including Cross-correlation (CC), Dynamic Time Warping (DTW), and Cross-Wavelet Analysis, to predict interaction levels using regression and classification techniques. We conducted a comparative study using both simulated data and real-world data involving sessions between teachers and children with and without autism to optimize the parameters for each algorithm and investigate different combinations of time series analysis. The results demonstrate that combining different time series analyses is advantageous, with Cross-wavelet Analysis (CWA) being particularly effective in quantifying interpersonal synchrony due to its ability to analyze synchrony across different frequencies. Additionally, a visualization tool is designed to display the dynamics of interaction over time and pairwise interactions conveniently. This paper provides a step-by-step guideline for studying interpersonal synchrony. It establishes a robust automated algorithm as a tool to track, visualize and understand social interactions, which can be easily adapted to a broader range of motor-coordination-related applications.

Objective: Panoramic basket catheters offer a rapid alternative to conventional local activation time (LAT) mapping for visualizing conduction patterns in arrhythmia patients. However, their limited spatial resolution often makes producing highly interpretable visualizations of the underlying phenomena challenging. We propose a novel interpolation method that addresses uncertainties arising from limited spatial resolution and variable tissue contact associated with basket catheters. By leveraging high sampling rates in the time domain to impose spatiotemporal constraints on the reconstruction, we enable enhanced spatial resolution in visualizing wave propagation. Methods: We constructed overlapping triangles from adjacent electrodes. The local apparent conduction velocity (CV) was estimated from cross-correlations of signals, and the triangles were classified into linear conducting, singularity, and non-conducting types based on CV and goodness of fit. Within each triangle, a linear constraint was imposed on the reconstruction, depending on the triangle type, by projecting signals along the CV. A smoothness constraint was added to ensure consistency at clique boundaries. The hyperparameters were calibrated in an unsupervised manner. Results: The proposed method reduced error by up to 16% compared to traditional interpolation methods in simulations and showed qualitative consistency with LAT maps in clinical cases. Conclusion: The method enhances spatial resolution in panoramic mapping, mitigating aliasing and signal artifacts, especially in regions with complex wavefronts or large interelectrode distances. Significance: The proposed method enables fast and accurate visualizations of panoramic conduction patterns from a single atrial cycle, potentially reducing procedure times compared to sequential mapping.

In 2021, approximately 537 million people were diagnosed with diabetes mellitus. With rates expected to rise, health expenditures are projected to reach one trillion USD by 2030. Thus, measuring glucose levels is essential for rationalizing the costs of public health systems. In this context, this paper presents two major contributions. First, it demonstrates that using deionized water (DI-water) as a reference for glucose sensing is not a reliable approach for representing human blood plasma (BP), as it lacks ions and suppresses essential effects such as losses. As an alternative, we investigate the use of an artificial blood plasma solution (ABPS) that closely resembles real human BP. Characterized over a range from 500 MHz to 10 GHz, ABPS shows marginal differences in real permittivity but significant differences in imaginary permittivity compared to DIwater. The second contribution is the design of a highly sensitive microwave resonator sensor based on concentric double circular split ring resonator (DCCSRR) on a ROGERS 5880 TM substrate. This sensor can differentiate glucose concentrations from 0 to 400 mg/dL, exceeding the relevant range for diabetic individuals (50-300 mg/dL). The DCCSRR operates at 2.48 GHz and can detect minimal concentration variations of 25 mg/dL in low concentrations, representing a significant advancement in the field. Differently from most sensitive approaches available to date, this structure operates in a non-licensed band and in a fully passive form, offering flexibility for implementation and low cost. These characteristics position it as a state-of-the-art solution in microwave glucose sensors. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The development of deep learning based image segmentation algorithms is often hindered by the lack of adequately annotated datasets, and this issue becomes a severe bottleneck in multi-class segmentation. The models can learn binary image segmentation since the datasets are often labeled for a single class. However, the task becomes more challenging when it comes to multi-class annotations and segmentation of regions of interest, e.g. in multi-organ segmentation that plays a vital role in computer assisted diagnosis, surgery planning, and other related applications. This study proposes a framework, CabiNet, that attempts to solve data bottleneck by learning multiclass healthy organ segmentation from low amount of partially annotated abdominal MRI data. Multiple organ-specific expert networks and a Jack of All (JoA) network are learned to generate posterior probability maps of individual organs simultaneously which are accumulated to get final posterior probability distributions over different organs for each pixel. On benchmark Combined Healthy Abdominal Organ Segmentation (CHAOS) MRI dataset CabiNet yielded very promising dice scores: liver (90.39%), right kidney (87.41%), left kidney (81.09%), spleen (90.78%) and overall average dice score as 87.4%.

Mobile Crowdsensing (MCS) is gaining attention for large-scale sensing that involves three types of entities: task requesters, workers equipped with sensing devices, and the platform that assigns tasks to workers considering their objectives and constraints. However, finding an allocation solution that satisfies the conditions above is NP-hard. A few studies suggested approximate solutions to this problem, focusing on one of the task's objectives: coverage maximization. Yet, they implement it in a single-task environment or with weak objective consideration, i.e., they consider other objectives, reducing the utility the task will receive. This study proposes a task allocation that focuses only on maximizing the task coverage, where we improved the solution to consider future task coverage possibilities. We consider an opportunistic MCS environment in which sensing has no impact on user trajectories. We assume a one-to-many matching where a task can be assigned to several workers, while a worker can be matched to at most one task. We first formulate the problem mathematically and prove it to be NP-hard. Then, we design three heuristic-based solutions that are more efficient and perform extensive performance evaluations based on a real-world dataset. Each solution improves the data quality and has a maximum execution time of milliseconds.