The physics remote laboratory: Implementation of an experiment on Standing Waves

There always have been some hurdles when it comes to the adequate use of didactical experimental activities in science education, such as the lack of proper training, insufficient time, and inadequate infrastructure. At this very moment, the pandemic has taught us that there may be also circumstances in which the traditional laboratory and the traditional activities are just not possible, thus online operable experiments might constitute a viable alternative for the practical lessons in higher education. In this paper, we discuss the development and the implementation of a remote-controlled didactical experiment on Standing Waves largely used in the physics basic program offered to the engineering courses. The development has combined applied knowledge from different areas, i.e. electric and electronics engineering, and computer science. In order to ascertain the experiment consistency, we have gathered data from the wave propagation speed and from the corresponding tension applied to the string and performed a χ-square linear fit in order to determine the correlation between the logarithm of both parameters. The experiment was successfully implemented and has been accessed by hundreds of different users from more than 30 different countries ever since. It has also been largely employed in practical activities at the university and has shown no significant signs of instability. It exhibited a total latency time inferior to 0.8 seconds on average and the results drawn from data it provides have shown to be accurate, within less than 0.8% of deviation with respect to the theoretical results.

B5G Ultrareliable Low Latency Networks for Efficient Secure Autonomous and Smart Internet of Vehicles

Recently, 5G and beyond 5G (B5G) systems, Ultrareliable Low Latency Network (URLLC) represents the key enabler for a range of modern technologies to support Industry 4.0 applications, such as transportation and healthcare. Real-world implementation of URLLC can help in major transformations in industries like autonomous driving, road safety, and efficient traffic management. Furthermore, URLLC contributes to the objective of fully autonomous cars on the road that can respond to dynamic traffic patterns by collaborating with other vehicles and surrounding environments rather than relying solely on local data. For this, the main necessity is that how information is to be transferred among the vehicles in a very small time frame. This requires information to be transferred among the vehicles reliably in extremely short time duration. In this paper, we have implemented and analyzed the Multiaccess Edge Computing- (MEC-) based architecture for 5G autonomous vehicles based on baseband units (BBU). We have performed Monte Carlo simulations and plotted curves of propagation latency, handling latency, and total latency in terms of vehicle density. We have also plotted the reliability curve to double-check our findings. When the RSU density is constant, the propagation latency is directly proportional to the vehicle density, but when the vehicle density is fixed, the propagation latency is inversely proportional. When RSU density is constant, vehicle density and handling latency are strictly proportional, but when vehicle density is fixed, handling latency becomes inversely proportional. Total latency behaves similarly to propagation latency; that is, it is also directly proportional.

LPE: Locality-based Dead Prediction in Exclusive TLB for Large Coverage

Translation lookaside buffer (TLB) is critical to modern multi-level memory systems’ performance. However, due to the limited size of the TLB itself, its address coverage is limited. Adopting a two-level exclusive TLB hierarchy can increase the coverage [M. Swanson, L. Stoller and J. Carter, Increasing TLB reach using superpages backed by shadow memory, 25th Annual Int. Symp. Computer Architecture (1998); H.P. Chang, T. Heo, J. Jeong and J. Huh Hybrid TLB coalescing: Improving TLB translation coverage under diverse fragmented memory allocations, ACM SIGARCH Comput. Arch. News 45 (2017) 444–456] to improve memory performance. However, after analyzing the existing two-level exclusive TLBs, we find that a large number of “dead” entries (they will have no further use) exist in the last-level TLB (LLT) for a long time, which occupy much cache space and result in low TLB hit-rate. Based on this observation, we first propose exploiting temporal and spatial locality to predict and identify dead entries in the exclusive LLT and remove them as soon as possible to leave room for more valid data to increase the TLB hit rates. Extensive experiments show that our method increases the average hit rate by 8.67%, to a maximum of 19.95%, and reduces total latency by an average of 9.82%, up to 24.41%.

Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

It's About Time: Minimizing Hardware and Software Latencies in Speech Research With Real-Time Auditory Feedback

Purpose Various aspects of speech production related to auditory–motor integration and learning have been examined through auditory feedback perturbation paradigms in which participants' acoustic speech output is experimentally altered and played back via earphones/headphones “in real time.” Scientific rigor requires high precision in determining and reporting the involved hardware and software latencies. Many reports in the literature, however, are not consistent with the minimum achievable latency for a given experimental setup. Here, we focus specifically on this methodological issue associated with implementing real-time auditory feedback perturbations, and we offer concrete suggestions for increased reproducibility in this particular line of work. Method Hardware and software latencies as well as total feedback loop latency were measured for formant perturbation studies with the Audapter software. Measurements were conducted for various audio interfaces, desktop and laptop computers, and audio drivers. An approach for lowering Audapter's software latency through nondefault parameter specification was also tested. Results Oft-overlooked hardware-specific latencies were not negligible for some of the tested audio interfaces (adding up to 15 ms). Total feedback loop latencies (including both hardware and software latency) were also generally larger than claimed in the literature. Nondefault parameter values can improve Audapter's own processing latency without negative impact on formant tracking. Conclusions Audio interface selection and software parameter optimization substantially affect total feedback loop latency. Thus, the actual total latency (hardware plus software) needs to be correctly measured and described in all published reports. Future speech research with “real-time” auditory feedback perturbations should increase scientific rigor by minimizing this latency.

A Memetic Algorithm for the Cumulative Capacitated Vehicle Routing Problem Including Priority Indexes

This paper studies the Cumulative Capacitated Vehicle Routing Problem, including Priority Indexes, a variant of the classical Capacitated Vehicle Routing Problem, which serves the customers according to a certain level of preference. This problem can be effectively implemented in commercial and public environments where customer service is essential, for instance, in the delivery of humanitarian aid or in waste collection systems. For this problem, we aim to minimize two objectives simultaneously, the total latency and the total tardiness of the system. A Mixed Integer formulation is developed and solved using the AUGMECON2 approach to obtain true efficient Pareto fronts. However, as expected, the use of commercial software was able to solve only small instances, up to 15 customers. Therefore, two versions of a Memetic Algorithm with Random Keys (MA-RK) were developed to solve the problem. The computational results show that both algorithms provided good solutions, although the second version obtained denser and higher quality Pareto fronts. Later, both algorithms were used to solve larger instances (20–100 customers). The results were mixed in terms of quality but, in general, the MA-RK v2 consistently outperforms the first version. The models and algorithms proposed in this research provide useful insights for the decision-making process and can be applied to solve a wide variety of business situations where economic, customer service, environmental, and social concerns are involved.

Measurements of Signal Delays in Software Defined Radio with Use of GNSS Modules

In the work a method of latency measurement in software defined radio (SDR) is proposed and validated. The test setup uses customer grade GNSS modules as reference time sources and enables relative delay calculation between signals received directly and those bypassed through SDR platform. The method is hardware agnostic in a sense, that it does not involve any custom software or hardware modifications. Tests that compare reported carrier-to-noise ratio and positioning errors were performed to prove functionality of such system. Additionally, authors measured several gnuradio blocks with respect to their impact on total latency introduced into signal path. All tests were performed on a bladeRF low-cost RF front-end. Minimum observed latency for the signal was below 10 ms.

Distributed Edge Computing to Assist Ultra-Low-Latency VANET Applications

Vehicular ad hoc networks (VANETs) are a recent class of peer-to-peer wireless networks that are used to organize the communication and interaction between cars (V2V), between cars and infrastructure (V2I), and between cars and other types of nodes (V2X). These networks are based on the dedicated short-range communication (DSRC) IEEE 802.11 standards and are mainly intended to organize the exchange of various types of messages, mainly emergency ones, to prevent road accidents, alert when a road accident occurs, or control the priority of the roadway. Initially, it was assumed that cars would only interact with each other, but later, with the advent of the concept of the Internet of things (IoT), interactions with surrounding devices became a demand. However, there are many challenges associated with the interaction of vehicles and the interaction with the road infrastructure. Among the main challenge is the high density and the dramatic increase of the vehicles’ traffic. To this end, this work provides a novel system based on mobile edge computing (MEC) to solve the problem of high traffic density and provides and offloading path to vehicle’s traffic. The proposed system also reduces the total latency of data communicated between vehicles and stationary roadside units (RSUs). Moreover, a latency-aware offloading algorithm is developed for managing and controlling data offloading from vehicles to edge servers. The system was simulated over a reliable environment for performance evaluation, and a real experiment was conducted to validate the proposed system and the developed offloading method.

Latency on a Stewart platform using washout filter

ABSTRACTThe aim of this work is to investigate and quantify the latency on a Stewart Platform caused exclusively by a Classic washout filter. This washout filter is intended to recreate the sensations of motion caused by changes of translational and rotational acceleration that an aircraft can provide, due to changes in attitudes caused by external factors, and those caused by the pilot’s command. The input signal was generated by a FlightGear Simulator in order to obtain the specific forces and angular velocities of a Boeing 747 during a take-off procedure. These signals are then filtered by a washout filter and sent to the inverse kinematics of the movable platform, which will transform the aircraft motion sensations in platforms actuator position, thereby causing a certain signal delay. Experiments were performed in a Stewart Platform to obtain the latency caused by the mathematical modelling of the entire washout filter system. This latency are then compared to the latency caused by the control and dynamics of the platform’s actuators. Results indicate that the washout filter is the most responsible for the latency of the specific force signals to be reproduced by the platform in this experiment, and that the natural frequency and damping coefficient values must be properly estimated in order to optimise the total latency.

AIMING: Resource Allocation with Latency Awareness for Federated-Cloud Applications

Federated-cloud has been widely deployed due to the growing popularity of real-time applications, and hence allocating resources among clouds becomes nontrivial to meet the stringent service requirements. The challenges lie in achieving minimized latency constrained by virtual machines rental overhead and resource requirement. This becomes further complicated by the issues of datacenter selection. To this end, we propose AIMING, a novel resource allocation approach which aims to minimize the latency constrained by monetary overhead in the context of federated-cloud. Specifically, the network resources are deployed and selected according to k-means clustering. Meanwhile, the total latency among datacenters is optimized based on binary quadratic programming. The evaluation is conducted with real data traces. The results show that AIMING can reduce total datacenter latency effectively compared with other approaches.