Search Results (4,986)

The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edge computing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of next-generation vehicular networks. Full article

The utilization of baffle structures as a highly effective strategy for mitigating debris flow has attracted significant scholarly attention in recent years. Although the predominant focus of existing research has been on augmenting the energy dissipation capabilities of baffle structures, their deformation behavior under impact load has not been extensively investigated. Addressing this research gap, the current study systematically designs a series of physical model experiments, incorporating variables such as baffle height, shape, and various combinations of baffle types to comprehensively analyze the deformation characteristics of baffles subjected to debris flow impact. The experimental results reveal that the deformation of baffle group structures demonstrates a marked non-uniform spatial distribution and exhibits a latency effect. Additionally, distinct baffle configurations show considerable variations in peak strain, suggesting that combining different baffle shapes can not only optimize energy dissipation but also enhance resistance to deformation. Moreover, the relationship between baffle height and the development of deformation in relation to energy dissipation capacity is inconsistent, indicating that deformation must be a key consideration in the design of baffle structures. Consequently, this paper advocates for the formulation of a deformation-based design strategy for baffle structures, with the findings presented herein providing a foundational reference for future studies. Full article

The latent reservoir remains a major roadblock to curing human immunodeficiency virus (HIV) infection. Currently available antiretroviral therapy (ART) can suppress active HIV replication, reduce viral loads to undetectable levels, and halt disease progression. However, antiretroviral drugs are unable to target cells that are latently infected with HIV, which can seed viral rebound if ART is stopped. Consequently, a major focus of the field is to study the latent viral reservoir and develop safe and effective methods to eliminate it. Here, we provide an overview of the major mechanisms governing the establishment and maintenance of HIV latency, the key challenges posed by latent reservoirs, small animal models utilized to study HIV latency, and contemporary cure approaches. We also discuss ongoing efforts to apply these approaches in combination, with the goal of achieving a safe, effective, and scalable cure for HIV that can be extended to the tens of millions of people with HIV worldwide. Full article

In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%. Full article

High-Level Synthesis (HLS) tools have revolutionized FPGA application development by providing a more efficient and streamlined approach, significantly impacting digital design methodologies. Despite the capability of FPGAs to customize numerical representations in data paths, most HLS projects have focused on fixed-point precision, while floating-point representations remain limited to vendor-provided single, double, and half-precision formats. This paper proposes a customized floating-point library compatible with HLS to address these limitations. This library allows programmers to define the number of exponent and mantissa bits at compile time, providing greater flexibility and enabling the use of mixed precision. Moreover, this library includes optimized implementations of common components such as vector summation (VSUM), dot-product (DP), and matrix-vector multiplication (MVM). Results demonstrate that the proposed library reduces latency and resource utilization compared to vendor IP blocks, particularly in VSUM, DP, and MVM operations. For example, the mvm operation involving a 32 × 32 matrix, using vendor IP requires 22 clock cycles, whereas CuFP completes the same task in just 7 clock cycles, using approximately 60% fewer DSPs, 10% fewer LUTs, and 60% fewer FFs. Full article

The runway detection algorithm for fixed-wing aircraft is a hot topic in the field of aircraft visual navigation. High accuracy, high fault tolerance, and lightweight design are the core requirements in the domain of runway feature detection. This paper aims to address these needs by proposing a lightweight runway feature detection algorithm named YOMO-Runwaynet, designed for edge devices. The algorithm features a lightweight network architecture that follows the YOMO inference framework, combining the advantages of YOLO and MobileNetV3 in feature extraction and operational speed. Firstly, a lightweight attention module is introduced into MnasNet, and the improved MobileNetV3 is employed as the backbone network to enhance the feature extraction efficiency. Then, PANet and SPPnet are incorporated to aggregate the features from multiple effective feature layers. Subsequently, to reduce latency and improve efficiency, YOMO-Runwaynet generates a single optimal prediction for each object, eliminating the need for non-maximum suppression (NMS). Finally, experimental results on embedded devices demonstrate that YOMO-Runwaynet achieves a detection accuracy of over 89.5% on the ATD (Aerovista Runway Dataset), with a pixel error rate of less than 0.003 for runway keypoint detection, and an inference speed exceeding 90.9 FPS. These results indicate that the YOMO-Runwaynet algorithm offers high accuracy and real-time performance, providing effective support for the visual navigation of fixed-wing aircraft. Full article

The Epstein–Barr virus (EBV) has a very high prevalence (>90% in adults), establishes a lifelong latency after primary infection, and exerts an oncogenic potential. This dsDNA virus encodes for various molecules, including microRNAs (miRs), which can be detected in the latent and lytic phases with different expression levels and affect, among others, immune evasion and malignant transformation. In this study, the different EBV miRs are quantified in EBV-positive lymphomas, and the impact on the host cell transcriptome of the most abundant EBV miRs will be analyzed using comparative RNA sequencing analyses. The EBV miRs ebv-miR-BART1, -BART4, -BART17, and -BHRF1-1 were most highly expressed, and their selective overexpression in EBV-negative human cells resulted in a large number of statistically significantly down- and up-regulated host cell genes. Functional analyses showed that these dysregulated target genes are involved in important cellular processes, including growth factor pathways such as WNT, EGF, FGF, and PDGF, as well as cellular processes such as apoptosis regulation and inflammation. Individual differences were observed between these four analyzed EBV miRs. In particular, ebv-miR-BHRF1-1 appears to be more important for malignant transformation and immune evasion than the other EBV miRs. Full article

Virus-associated chronic inflammation may contribute to autoimmunity in a number of diseases. In the brain, autoimmune encephalitis appears related to fluctuating reactivation states of neurotropic viruses. In addition, viral miRNAs and proteins can be transmitted via exosomes, which constitute novel but highly relevant mediators of cellular communication. The current study questioned the role of HSV-1-encoded and host-derived miRNAs in cerebrospinal fluid (CSF)-derived exosomes, enriched from stress-induced neuroinflammatory diseases, mainly subarachnoid hemorrhage (SAH), psychiatric disorders (AF and SZ), and various other neuroinflammatory diseases. The results were compared with CSF exosomes from control donors devoid of any neuroinflammatory pathology. Serology proved positive, but variable immunity against herpesviruses in the majority of patients, except controls. Selective ultrastructural examinations identified distinct, herpesvirus-like particles in CSF-derived lymphocytes and monocytes. The likely release of extracellular vesicles and exosomes was most frequently observed from CSF monocytes. The exosomes released were structurally similar to highly purified stem-cell-derived exosomes. Exosomal RNA was quantified for HSV-1-derived miR-H2-3p, miR-H3-3p, miR-H4-3p, miR-H4-5p, miR-H6-3p, miR-H27 and host-derived miR-21-5p, miR-146a-5p, miR-155-5p, and miR-138-5p and correlated with the oxidative stress chemokine IL-8 and the axonal damage marker neurofilament light chain (NfL). Replication-associated miR-H27 correlated with neuronal damage marker NfL, and cell-derived miR-155-5p correlated with oxidative stress marker IL-8. Elevated miR-138-5p targeting HSV-1 latency-associated ICP0 inversely correlated with lower HSV-1 antibodies in CSF. In summary, miR-H27 and miR-155-5p may constitute neuroinflammatory markers for delineating frequent and fluctuating HSV-1 replication and NfL-related axonal damage in addition to the oxidative stress cytokine IL-8 in the brain. Tentatively, HSV-1 remains a relevant pathogen conditioning autoimmune processes and a psychiatric clinical phenotype. Full article

Nosocomial infections, a prevalent issue in intensive care units due to antibiotic overuse, could potentially be addressed by metal oxide nanoparticles (NPs). However, there is still no comprehensive understanding of the impact of NPs’ size on their antibacterial efficacy. Therefore, this study provides a novel investigation into the impact of ZnO NPs’ size on bacterial growth kinetics. NPs were synthesized using a sol–gel process with monoethanolamine (MEA) and water. X-ray diffraction (XRD), transmission electron microscopy (TEM), and Raman spectroscopy confirmed their crystallization and size variations. ZnO NPs of 22, 35, and 66 nm were tested against the most common nosocomial bacteria: Escherichia coli, Pseudomonas aeruginosa (Gram-negative), and Staphylococcus aureus (Gram-positive). Evaluation of minimum inhibitory and bactericidal concentrations (MIC and MBC) revealed superior antibacterial activity in small NPs. Bacterial growth kinetics were monitored using optical absorbance, showing a reduced specific growth rate, a prolonged latency period, and an increased inhibition percentage with small NPs, indicating a slowdown in bacterial growth. Pseudomonas aeruginosa showed the lowest sensitivity to ZnO NPs, attributed to its resistance to environmental stress. Moreover, the antibacterial efficacy of paint containing 1 wt% of 22 nm ZnO NPs was evaluated, and showed activity against E. coli and S. aureus. Full article

To mitigate the control overhead of Software-Defined Mobile Ad Hoc Networks (SD-MANETs), this paper proposes a novel approach, termed Software-Defined Greedy Perimeter Stateless Routing (SD-GPSR), which integrates geographical location information. SD-GPSR optimizes routing functions by decentralizing them within the data plane of SD-MANET, utilizing the geographic location information of nodes to enhance routing efficiency. The controller is primarily responsible for providing location services and facilitating partial centralized decision-making. Within the data plane, nodes employ an enhanced distance and angle-based greedy forwarding algorithm, denoted as GPSR_DA, to efficiently forward data. Additionally, to address the issue of routing voids in the data plane, we employ the A* algorithm to compute an optimal routing path that circumvents such voids. Finally, we conducted a comparative analysis with several state-of-the-art approaches. The evaluation experiments demonstrate that SD-GPSR significantly reduces the control overhead of the network. Simultaneously, there is a notable improvement in both end-to-end latency and packet loss rate across the network. Full article

With the gradual increase in the proportion of new energy sources in the power grid, there is an urgent need for more flexible resources to participate in short-term regulation. The impact of communication network channel quality will continue to magnify, and factors such as communication latency may directly affect the efficiency and effectiveness of resource regulation. In this context of a large number of flexible demand-side resources accessing the grid, this article proposes a bidirectional channel delay measurement method based on MQTT (Message Queuing Telemetry Transport). It can effectively evaluate the real-time performance of communication links, considering that resources mainly access the grid through the public network. Subsequently, focusing on two typical types of resources on the demand side, namely, split air conditioners and central air conditioners, this article proposes an assessment method for correcting the response capabilities of air conditioning resources considering communication latency. Experimental simulations are conducted, and the results demonstrate that under given communication conditions, this method can more accurately estimate the response capability of air conditioners. This provides a basis for formulating more reasonable scheduling strategies, avoiding excessive or insufficient resource regulation caused by communication issues, and aiding the power grid in achieving precise scheduling. Full article

This study proposes an edge computing-based facial expression recognition system that is low cost, low power, and privacy preserving. It utilizes a minimally obtrusive cap-based system designed for the continuous and real-time monitoring of a user’s facial expressions. The proposed method focuses on detecting facial skin deformations accompanying changes in facial expressions. A multi-zone time-of-flight (ToF) depth sensor VL53L5CX, featuring an 8 × 8 depth image, is integrated into the front brim of the cap to measure the distance between the sensor and the user’s facial skin surface. The distance values corresponding to seven universal facial expressions (neutral, happy, disgust, anger, surprise, fear, and sad) are transmitted to a low-power STM32F476 microcontroller (MCU) as an edge device for data preprocessing and facial expression classification tasks utilizing an on-device pre-trained deep learning model. Performance evaluation of the system is conducted through experiments utilizing data collected from 20 subjects. Four deep learning algorithms, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNN), are assessed. These algorithms demonstrate high accuracy, with CNN yielding the best result, achieving an accuracy of 89.20% at a frame rate of 15 frames per second (fps) and a maximum latency of 2 ms. Full article

With the increasing complexity of applications, many delay-sensitive and compute-intensive services have posed significant challenges to mobile devices. Addressing how to efficiently allocate heterogeneous network resources to meet the computing and delay requirements of terminal services is a pressing issue. In this paper, a new cooperative twin delayed deep deterministic policy gradient and deep-Q network (TD3-DQN) algorithm is introduced to minimize system latency by optimizing computational offloading and caching placement asynchronously. Specifically, the task-partitioning technique divides computing tasks into multiple subtasks, reducing the response latency. A DQN intelligent algorithm is presented to optimize the offloading path to edge servers by perceiving network resource status. Furthermore, a TD3 approach is designed to optimize the cached content in the edge servers, ensuring dynamic popularity content requirements are met without excessive offload decisions. The simulation results demonstrate that the proposed model achieves lower latency and quicker convergence in asymmetrical cloud–edge collaborative networks compared to other benchmark algorithms. Full article

This comprehensive review explores the advancements in processing-in-memory (PIM) techniques and chiplet-based architectures for deep neural networks (DNNs). It addresses the challenges of monolithic chip architectures and highlights the benefits of chiplet-based designs in terms of scalability and flexibility. This review emphasizes dataflow-awareness, communication optimization, and thermal considerations in PIM-enabled manycore architectures. It discusses tailored dataflow requirements for different machine learning workloads and presents a heterogeneous PIM system for energy-efficient neural network training. Additionally, it explores thermally efficient dataflow-aware monolithic 3D (M3D) NoC architectures for accelerating CNN inferencing. Overall, this review provides valuable insights into the development and evaluation of chiplet and PIM architectures, emphasizing improved performance, energy efficiency, and inference accuracy in deep learning applications. Full article

Massive MIMO (Multiple Input Multiple Output) systems impose significant processing burdens along with strict latency requirements. The combination of large-scale antenna arrays and wide bandwidth requirements for next-generation wireless systems creates an exponential increase in frontend to backend data. Balancing the processing latency and reliability is critical for baseband processing tasks such as QAM detection. While linear detection algorithms have low computational complexity, their use in Massive MIMO scenario has heavy degradation in error performance. Nonlinear detection methods such as Maximum Likelihood and Sphere Decoding have good error performance, but they suffer from high, variable, and uncontrollable computational complexity. For such cases, the K-best QAM detection algorithm can provide required control over the system performance while maintaining near-ML error performance. In this paper, hard-output, as well as soft-output K-best QAM detection, is implemented in a CPU by utilizing the multiple cores combined with vector processing. Similarly, hard-output detection in a GPU is implemented by leveraging the SIMD (Single Instruction, Multiple Data) architecture and Warp-based execution model. The processing time per bit and the energy consumption per bit are compared for CPU and GPU implementations for QAM constellation density and MIMO array size. The GPU implementation shows up to 5× processing latency per bit improvement and up to 120× energy consumption per bit improvement over the CPU implementation for typical QAM constellations such as 4, 16, and 64 QAM. GPU implementation also shows up to 125× improvement over CPU implementation in energy consumption per bit for larger MIMO configurations such as 24 × 24 and 32 × 32. Finally, the soft-output detector is combined with a LDPC (Low-Density Parity Check) decoder to obtain the FER (Frame Error Rate) performance for CPU implementation. The FER is then combined with frame processing latency to form a Goodput metric to demonstrate the latency and reliability tradeoff. Full article