Commercial sensors, while dependable in providing single-point data, command a high acquisition cost, in stark contrast to low-cost sensors, which are readily available in greater numbers. This enables more extensive temporal and spatial data collection, though with potentially diminished accuracy. For short-term, limited-budget projects eschewing high data accuracy, the deployment of SKU sensors is suggested.
Wireless multi-hop ad hoc networks commonly utilize the time-division multiple access (TDMA) medium access control (MAC) protocol to manage access conflicts. Precise time synchronization amongst the nodes is critical to the protocol's effectiveness. This paper proposes a novel time synchronization protocol for cooperative TDMA multi-hop wireless ad hoc networks, also known as barrage relay networks (BRNs). Time synchronization messages are sent via cooperative relay transmissions, which are integral to the proposed protocol. We detail a network time reference (NTR) selection procedure that is expected to yield faster convergence and a reduced average timing error. The NTR selection procedure entails each node capturing the user identifiers (UIDs) of other nodes, the calculated hop count (HC) to itself, and the node's network degree, which quantifies its immediate neighbors. In order to establish the NTR node, the node exhibiting the smallest HC value from the remaining nodes is chosen. If a minimum HC is reached by several nodes, the NTR node is selected from amongst these nodes based on the larger degree. This paper proposes a new time synchronization protocol with NTR selection for cooperative (barrage) relay networks, as per our knowledge, for the first time. The proposed time synchronization protocol's average time error is tested within a range of practical network conditions via computer simulations. Furthermore, we juxtapose the performance of the proposed protocol with established time synchronization techniques. Empirical results demonstrate the proposed protocol's superior performance compared to conventional methods, showcasing significant reductions in average time error and convergence time. The robustness of the proposed protocol to packet loss is also apparent.
This research paper investigates a robotic computer-assisted implant surgery motion-tracking system. Problems can stem from inaccurate implant positioning, thus a precise real-time motion-tracking system is critical in computer-assisted implant surgery to prevent these complications. The study of essential motion-tracking system elements, including workspace, sampling rate, accuracy, and back-drivability, are categorized and analyzed. Employing this analysis, the motion-tracking system's expected performance criteria were ensured by defining requirements within each category. The proposed 6-DOF motion-tracking system exhibits high accuracy and back-drivability, and is therefore deemed suitable for computer-aided implant surgery. The proposed system's ability to achieve the fundamental motion-tracking features essential for robotic computer-assisted implant surgery has been validated by the experimental findings.
By altering the tiny frequency shifts on the array's elements, a frequency-diverse array (FDA) jammer can craft multiple misleading range targets. Many countermeasures to deceptive jamming against SAR systems utilizing FDA jammers have been studied extensively. However, the FDA jammer's capability to produce a significant level of jamming, including barrage jamming, has been rarely noted. this website The paper describes a novel barrage jamming method for SAR utilizing an FDA jammer. The stepped frequency offset of the FDA is incorporated to establish range-dimensional barrage patches, achieving a two-dimensional (2-D) barrage effect, with micro-motion modulation further increasing the extent of the barrage patches in the azimuthal direction. Mathematical derivations and simulation results provide compelling evidence for the proposed method's capability to generate flexible and controllable barrage jamming.
Cloud-fog computing encompasses a wide array of service environments, providing agile, rapid services to customers, while the burgeoning Internet of Things (IoT) generates a substantial quantity of data daily. The provider, to meet service level agreements (SLAs) and complete IoT tasks, skillfully manages the allocation of resources and utilizes optimized scheduling methods within fog or cloud-based systems. The efficiency of cloud services is directly affected by crucial variables, such as energy consumption and cost, often neglected in existing assessment methodologies. To overcome the challenges presented previously, an efficient scheduling algorithm is essential to effectively manage the heterogeneous workload and raise the quality of service (QoS). Within the context of this paper, a multi-objective task scheduling algorithm, the Electric Earthworm Optimization Algorithm (EEOA), inspired by nature, is formulated for handling IoT requests in a cloud-fog system. The electric fish optimization algorithm (EFO) was augmented by the integration of the earthworm optimization algorithm (EOA) in this method, designed to find the best solution to the issue at hand. Significant real-world workloads, exemplified by CEA-CURIE and HPC2N, were used to evaluate the suggested scheduling technique's performance metrics, including execution time, cost, makespan, and energy consumption. Using diverse benchmarks and simulation results, our proposed algorithm surpasses existing methods, achieving an 89% efficiency increase, a 94% decrease in energy use, and a 87% decrease in overall costs across the examined scenarios. The suggested scheduling approach, as demonstrated by detailed simulations, consistently outperforms existing techniques.
The methodology of characterizing ambient seismic noise in an urban park, as presented in this study, utilizes two Tromino3G+ seismographs. These seismographs capture simultaneous high-gain velocity recordings along north-south and east-west axes. We aim to establish design parameters for seismic surveys conducted at a site before the permanent seismograph deployment is undertaken. Ambient seismic noise, the coherent element within measured seismic signals, encompasses signals from unregulated, both natural and man-made, sources. Geotechnical research, simulations of seismic infrastructure behavior, surface observations, soundproofing methodologies, and urban activity monitoring all have significant application. This endeavor might involve the use of numerous seismograph stations positioned throughout the target area, with data collected across a period of days to years. Realistically, a well-distributed array of seismographs might not be a viable option for all places. Thus, characterizing ambient seismic noise in urban contexts and the resulting limitations of reduced station numbers, in cases of only two stations, are vital. The continuous wavelet transform, peak detection, and event characterization comprise the developed workflow. The criteria for classifying events include amplitude, frequency, time of occurrence, the azimuth of the source relative to the seismograph, duration, and bandwidth. this website Results from various applications will influence the decision-making process in selecting the seismograph's sampling frequency, sensitivity, and appropriate placement within the focused region.
This paper showcases the implementation of an automated procedure for 3D building map reconstruction. this website A significant innovation of this method is the addition of LiDAR data to OpenStreetMap data, enabling automated 3D reconstruction of urban environments. The area requiring reconstruction, delineated by its enclosing latitude and longitude points, constitutes the exclusive input for this method. The OpenStreetMap format is used to acquire data for the area. Despite the comprehensive nature of OpenStreetMap, some constructions, such as buildings with distinct roof types or varied heights, are not fully represented. The missing parts of OpenStreetMap data are filled through the direct analysis of LiDAR data with a convolutional neural network. The presented approach showcases the potential of a model to be created using only a few urban roof samples from Spain, enabling accurate predictions of roofs in additional Spanish and international urban environments. Our analysis of the results indicates a mean height value of 7557% and a mean roof value of 3881%. Consequent to the inference process, the obtained data augment the 3D urban model, leading to accurate and detailed 3D building maps. This research showcases the neural network's aptitude for locating buildings that are missing from OpenStreetMap databases but are present in LiDAR scans. A subsequent exploration of alternative approaches, such as point cloud segmentation and voxel-based techniques, for generating 3D models from OpenStreetMap and LiDAR data, alongside our proposed method, would be valuable. To improve the size and stability of the training data set, exploring data augmentation techniques is a subject worthy of future research consideration.
The integration of reduced graphene oxide (rGO) structures within a silicone elastomer composite film yields soft and flexible sensors, appropriate for wearable applications. Upon pressure application, the sensors exhibit three distinct conducting regions that signify different conducting mechanisms. This article seeks to illuminate the conduction methods within these composite film sensors. Investigations led to the conclusion that Schottky/thermionic emission and Ohmic conduction largely determined the characteristics of the conducting mechanisms.
We propose a system, leveraging deep learning and a phone, to evaluate dyspnea using the mMRC scale, detailed in this paper. A key aspect of the method is the modeling of subjects' spontaneous reactions while they perform controlled phonetization. Designed, or painstakingly selected, these vocalizations aimed to counteract stationary noise in cell phones, induce varied exhalation rates, and encourage differing levels of fluency in speech.