Pipeline and void recognition are two key tasks in the underground monitoring of urban roads. Ground penetrating radar (GPR), as an effective geophysical method, plays an important role in this area. With the increasing amount of GPR data, automatic recognition has become a research hotspot. However, existing automated recognition methods still suffer from low accuracy or high dependence on datasets. In this paper, a non-learning method for both pipeline and void recognition is proposed. In this method, the B-scan is preprocessed by removing the direct coupled wave and multiple echoes.Then, the binarized image is converted to sparse image using non-zero interval sparse coding (NISE). Next, the slope distribution of sparse images is extracted using column offset coding (COE). And clustering is carried out according to the corresponding relationship between the image slope and its original position. Finally, the decision is made according to the slope distribution characteristics of each cluster. The proposed method was tested on both simulated and field data. Experimental results show that the method not only has the advantage of being training-free but also exhibits excellent recognition accuracy.

With the vast demand in marine development, robotic fish show promising potential in underwater exploration for their high-performance propulsion ability. However, fish-inspired robots are yet to utilize the structural flexibility of rhythmic actuation such as bony fish (Osteichthyes). The Body and Caudal Fin (BCF) locomotion in fish optimizes the use of muscle power and body flexibility by synchronizing muscle activation with the undulating-oscillatory tail-flapping, such as Thunniform, while robotic fish are primarily designed as motion trackers rather than as efficient swimmers. In this paper, we propose a power allocation strategy (PAS) that imitates muscle rhythmic actuation, which increases the flapping amplitude by the coupling of the peduncle motion and the tail deformation. Inspired by this peduncle-tail mechanism, we developed a Direct-Drive Fish Robot (DDRFishBot). The DDRFishBot is enhanced by our developed PAS in Tail-Elastic Potential Energy (T-EPE) release by 228%, in propulsion by 45.6% and in efficiency coefficient by 16.3%. This study establishes the performance enhancement principle of exploiting tail flexibility through a simple scotch yoke mechanism, expanding the performance space of fish-inspired tail-flapping swimming robot.

Measuring visitor engagement in informal learning environments presents critical challenges for optimizing educational experiences and spatial design. While existing research predominantly focuses on formal settings, systematic analysis of multidimensional engagement in complex environments like museums remains underdeveloped. This study introduces the first integrated head-mounted display (HMD)-based framework combining meso-scale spatial analysis through behavioral engagement heatmaps with micro-level temporal engagement modeling via headset pose and eye-tracking data. Our edge-optimized long short-term memory (LSTM) model achieves real-time engagement measurement with a mean squared error (MSE) of 0.145 using four physiologically grounded features from a two-stage user study (N=20 for feature analysis, N=15 for modeling). The framework synthesizes planar trajectory heatmaps and panoramic fixation distributions to enable both real-time adaptive support and post-hoc exhibition design insights. Results demonstrate HMDs' potential as precision measurement tools, establishing methodological foundations for intelligent heritage environments that dynamically respond to engagement states through integrated meso-micro analytics.

Cloud systems deploy a wide variety of shared resources and host a large number of tenant applications. To perform cloud research, a small experimental platform is commonly used, which hides the huge system complexity and provides flexibility. Despite being simpler, this platform should include the main cloud system components (hardware and software) to provide representative results. A wide set of platforms have spread in recent years; however, most of them only include a major cloud component or lack the deployment of virtual machines (VMs) to provide isolation. This paper presents Stratus, an experimental platform that is currently being used to carry out cloud research. To the best of our knowledge, Stratus is the only platform that jointly provides three main features: uses VMs to isolate tenant applications, deploys the three types of cloud nodes (server, client, and storage), and manages all main shared system resources (CPUs, LLC space, memory, network, and disk bandwidth). Moreover, Stratus implements a software manager to ease the research and aid the design of QoS-aware policies. The manager integrates three main functionalities: management and control of the execution of VMs and running applications, monitoring of hardware performance counters and system resource utilization, and partitioning of the main shared system resources by using technologies available in commercial processors.

The increasing popularity of cloud computing has forced cloud providers to build economies of scale to meet the growing demand. Nowadays, data-centers include thousands of physical machines, each hosting many virtual machines (VMs), which share the main system resources, causing interference that can significantly impact on performance. Frequently, these data-centers run latency-critical workloads, whose performance is determined by tail latency, which is very sensitive to the interference of co-running workloads. To prevent QoS violations, cloud providers adopt overprovisioning strategies but they reduce the server utilization and increase the costs. A mechanism that accurately estimates performance degradation dynamically in a production system would allow cloud providers to improve the servers’ utilization. In this work we propose Cloud White, an approach that is able to detect the inter-VM interference in scenarios with multiple co-located latency-critical VMs and estimate the performance degradation using multi-variable regression models. Unlike previous proposals, Cloud White is built taking into account the limitations of a public cloud production system. Experimental results show that Cloud White is able to estimate performance degradation with a small overall prediction error of 5%.

A novel method is proposed to extend the output power back-off (OPBO) range of the Doherty power amplifier (DPA). This study reveals that the OPBO range of the DPA can be extended by tuning the output impedance of the peaking stage away from infinity and changing the phase delay of the output matching network of the carrier power amplifier. Based on this theory, a large-OPBO-range high-efficiency asymmetrical DPA working band from 1.55 to 2.2 GHz (35% relative bandwidth) is designed to verify the proposed method. Experimental results show that the DPA operates from 1.6 to 2.1 GHz. The range of the measured efficiency is 42.2%–52.1% in the OPBO state and 47%–62.7% in the saturation state. The OPBO range is 11.1–13.2 dB.

Proprioception and variable stiffness are two trending topics in soft robotics research. The former could endow soft robots with the ability to perceive the environment as well as their internal states without the need of dedicated sensors, while the latter could strengthen the otherwise excessive compliance, enabling soft robots for tasks which require a higher force. Both directions have been extensively reported in existing literature, achieving both concurrently was even more challenging. The major limiting factor was the limited stiffness due to the hyper elasticity of conventional soft robots, which increases the difficulties in capturing the continues deformation. In this work, we proposed an alternative approach to tackle these two challenges, a novel “tune-down” approach, combining proprioception with stiffness regulation and implemented over-constrained soft robotic joint designs to further strengthen this spirit. As a result, the soft robotic joint could achieve multi-directional proprioception, as well as variable stiffness tuning, concurrently, using merely an on-board sensor for basic pneumatic control. The concept, design, modeling, actuation/control, and experimental validation were presented in detail, demonstrating the efficacy and potential of the proposed approach.

Multithreaded latency-critical applications represent an important subset of workloads running on public cloud systems. Most of these systems deploy powerful computing servers including Intel Hyper-Threading processors. Understanding how performance is affected by the consumption of the main system resources is a major concern for cloud providers in order to devise virtualization strategies that improve the system efficiency. With this aim, this paper first characterizes the impact of QPS on tail latency, analyzing different scenarios varying the number of threads and the thread-to-core allocation (single-task and multi-task execution) policy. The characterization study reveals that the performance of some applications does not scale with the number of threads, and the performance of some others is insensitive to the Hyper-Threading technology, so they can be allocated in less physical cores and improve system utilization. Identifying these applications, however, at run-time is challenging. Despite identifying these applications at run-time is challenging, this paper shows that they can be successfully detected at run-time by analyzing the utilization trend of the major system resources. In addition to CPU, we have also studied how assigning the share of each application of other major shared system resources impacts on performance. We outline considerations cloud providers should take into account to improve performance and resource utilization.