This paper presents an innovative robotic inspection system designed to enhance the detection and analysis of structural defects in concrete infrastructure. The proposed inspection system is comprised of three modules: a robotic data collection module, a visual inspection module, and a subsurface mapping module. The robotic data collection module features an omnidirectional robotic platform, designed to move sideways without spinning. It is equipped with Ground Penetrating Radar (GPR) and RGB-D cameras, facilitating systematic data collection across construction sites. The visual inspection module employs a learning-based method, InspectionNet++, to analyze the frames for surface defects such as cracks, spalls, and stains, providing high accuracy and metric measurements of the defects. The subsurface mapping module processes the GPR data to detect and visualize hidden defects, creating a comprehensive map that correlates these with visible surface anomalies. Field tests demonstrate the system’s ability to automate construction structural inspection with improved efficiency and precision. Additionally, the customized visualization software is introduced to enable intuitive and interactive exploration of the detected defects within a unified interface. By automating data collection and enhancing defect detection through learning algorithms, the system not only speeds up the inspection process but also increases the reliability of infrastructure evaluations, supporting more informed maintenance decisions. Note to Practitioners—This paper introduces a robotic solution for inspection and condition assessment of concrete infrastructure. The system uses an omnidirectional robot equipped with GPR and RGB-D cameras to automatically collect data across construction sites. By harnessing vision-based positioning technology, our system empowers the robot to scan the ground surface in free motion pattern. This eliminates the need for time-consuming grid line setup traditionally required for manual GPR data collection. Our approach combines multi-sensor data analytics with advanced software, which enables the detection and visualization of both surface defects (cracks, spalls, stains) and subsurface anomalies. For practitioners, this automated approach offers several key benefits over manual inspections: increased inspection speed and coverage, rapid data collection enabled by robotic free motion, higher detection accuracy, quantitative defect measurements, and unified visualization correlating surface/subsurface conditions. This system has the potential to revolutionize infrastructure assessment practices. By facilitating more frequent and reliable inspections with minimal human intervention, it paves the way for proactive maintenance and ensures the sustainability of critical infrastructure. A current limitation is the relatively small training dataset for visual inspection, which may affect generalizability across diverse concrete structures. Future work aims to expand the dataset, improve irrelevant feature filtering, and utilize other NDE sensors (e.g., impact echo) for infrastructure inspection.

Genome sequence analysis is fundamental to medical breakthroughs such as developing vaccines, enabling genome editing, and facilitating personalized medicine. The exponentially expanding sequencing datasets and complexity of sequencing algorithms necessitate performance enhancements. While the performance of software solutions is constrained by their underlying hardware platforms, the utility of fixed-function accelerators is restricted to only certain sequencing algorithms.This paper presents QUETZAL, the first general-purpose vector acceleration framework designed for high efficiency and broad applicability across a diverse set of genomics algorithms. While a commercial CPU’s vector datapath is a promising candidate to exploit the data-level parallelism in genomics algorithms, our analysis finds that its performance is often limited due to long-latency scatter/gather memory instructions. QUETZAL introduces a hardware-software co-design comprising an accelerator microarchitecture closely integrated with the CPU’s vector datapath, alongside novel vector instructions to fully capitalize on the proposed hardware. QUETZAL integrates a set of scratchpad-style buffers meticulously designed to minimize latency associated with scatter/gather instructions during the retrieval of input genome sequences data. QUETZAL supports both short and long reads, and different types of sequencing data formats. A combination of hardware and software techniques enables QUETZAL to reduce the latency of memory instructions, perform complex computation using a single instruction, and transform data representations at runtime, resulting in overall efficiency gain. QUETZAL significantly accelerates a vectorized CPU baseline on modern genome sequence analysis algorithms by 5.7×, while incurring a small area overhead of 1.4% post place-and-route at the 7nm technology node compared to an HPC ARM CPU.

The maturity level of RISC-V and the availability of domain-specific instruction set extensions, like vector processing, make RISC-V a good candidate for supporting the integration of specialized hardware in processor cores for the High Performance Computing (HPC) application domain. In this article, 1 we present Vitruvius+, the vector processing acceleration engine that represents the core of vector instruction execution in the HPC challenge that comes within the EuroHPC initiative. It implements the RISC-V vector extension (RVV) 0.7.1 and can be easily connected to a scalar core using the Open Vector Interface standard. Vitruvius+ natively supports long vectors: 256 double precision floating-point elements in a single vector register. It is composed of a set of identical vector pipelines (lanes), each containing a slice of the Vector Register File and functional units (one integer, one floating point). The vector instruction execution scheme is hybrid in-order/out-of-order and is supported by register renaming and arithmetic/memory instruction decoupling. On a stand-alone synthesis, Vitruvius+ reaches a maximum frequency of 1.4 GHz in typical conditions (TT/0.80V/25°C) using GlobalFoundries 22FDX FD-SOI. The silicon implementation has a total area of 1.3 mm 2 and maximum estimated power of ∼920 mW for one instance of Vitruvius+ equipped with eight vector lanes.

It is possible to find any kind of useful information in the Web. However, there are serious problems in retrieving, managing and using this information, due to its vastness. Different approaches have been developed to avoid those problems (search engines, metasearch engines, spiders, softbots, intelligent agents or Web agents). This paper is based on one of these systems which uses a set of heterogeneous intelligent software agents to achieve these previous tasks. Two different agents compose the system: Web agents developed to retrieve information from a specific Web source and meta Web agents developed to select the appropriated Web agent to search the necessary information. Each Web agent retrieves, filters and stores the information from the Web to improve system performance. The meta Web agents need to represent and classify the behavior of different Web agents. In this work a fuzzy system that helps to classify the behavior of the Web agents is presented. The meta Web agent calculates the appropriateness of existing agent behavior, using different distances that are analyzed in the paper. The behavior classification is used to decide which Web agent is requested for information by the meta Web agent.