Orthogonal Time Frequency Space Modulation in 6G Era

Over the past decades, wireless technologies with reliable data transmissions and precise sensing ability in hostile environments are much less understood and significantly under-developed. The DD domain waveform, e.g., orthogonal time frequency space(OTFS), has been recognized globally for its great potential to achieve high-reliable communications and robust sensing performance under various channel conditions in 6G era.

The Design of VLC-PLC System for Substation Inspection

A kind of integrated network architecture visible light communication (VLC) and power line communication (PLC) is put forward. This architecture is low cost and easy to implement which overcomes the shortcoming of the traditional network architecture. Furthermore, the VLC-PLC integration technology is applied to typical power grid business scene, which is substation intelligent inspection. The business process of master station platform is analyzed. During the intelligent inspection, the VLC-PLC system provides voice communication for on-site inspection personnel and management personnel, and position service. The system can ensure the safety and security of power production.

A Key Management Scheme Based on Hypergraph for Fog Computing

Fog computing is a new paradigm supporting the stringent requirements of mobility applications by bridging cloud computing and smart devices. Since the smart devices may be deployed in dynamic areas where are out of strict monitoring and protection, fog computing requires security protections to ensure confidentiality and integrity. In this article, to deal with security requirements and considering the distinctive features, a key management based on hypergraph schemed is designed. Firstly, based on the key hypergraph, the three hierarchy architecture of fog computing is divided into two subnetworks. Furthermore, each key management process of both two subnetworks is designed to satisfy the operational and security requirements of fog computing. Finally, the performance evaluation and numerical simulation have been provided to validate the proposed scheme.

Efficient pipelined flow classification for intelligent data processing in IoT

The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things(IoT).In recent years,researchers have tried to develop hardware-based solutions for the classification of Internet packets.Due to higher throughput and shorter delays,these solutions are considered as a major key to improving the quality of services.Most of these efforts have attempted to implement a software algorithm on the FPGA to reduce the processing time and enhance the throughput.The proposed architectures,however,cannot reach a compromise among power consumption,memory usage,and throughput rate.In view of this,the architecture proposed in this paper contains a pipelinebased micro-core that is used in network processors to classify packets.To this end,three architectures have been implemented using the proposed micro-core.The first architecture performs parallel classification based on header fields.The second one classifies packets in a serial manner.The last architecture is the pipeline-based classifier,which can increase performance by nine times.The proposed architectures have been implemented on an FPGA chip.The results are indicative of a reduction in memory usage as well as an increase in speedup and throughput.The architecture has a power consumption of is 1.294w,and its throughput with a frequency of 233 MHz exceeds 147 Gbps.

Process for Compensating Local Magnetic Perturbations on Ferromagnetic Surfaces

This paper addresses a practical application for the compensation of a local magnetic perturbation in a ship in the near field region. The process will avoid expensive deperming techniques usually applied to ships to treat magnetic anomalies. The technique includes a new system to construct magnetic maps on flat ferromagnetic surfaces. Once the magnetic maps are obtained, a new system is proposed to evaluate and locate local magnetic perturbations. Once the local perturbations are located, they are compensated by local degaussing coils. The new technique has been applied to the detection of local magnetic perturbations in four naval platforms. Two of them presented important magnetic anomalies, and were successfully detected and corrected by applying the new technique, thus showing its practical value.

Template-based AADL automatic code generation

Embedded real-time systems employ a variety of operating system platforms. Consequently, for automatic code generation, considerable redevelopment is needed when the platform changes. This results in major challenges with respect to the automatic code generation process of the architecture analysis and design language (AADL). In this paper, we propose a method of template-based automatic code generation to address this issue. Templates are used as carriers of automatic code generation rules from AADL to the object platform. These templates can be easily modified for different platforms. Automatic code generation for different platforms can be accomplished by formulating the corresponding generation rules and transformation templates. We design a set of code generation templates from AADL to the object platform and develop an automatic code generation tool. Finally, we take a typical data processing unit (DPU) system as a case study to test the tool. It is demonstrated that the autogenerated codes can be compiled and executed successfully on the object platform.

Energy Management Strategy Considering Multi-time-scale Operational Modes of Batteries for the Grid-connected Microgrids Community

The advantages and promoting applications of the microgrids community(MGC)allows for a critical step being taken toward a smart grid.An energy management strategy(EMS)is essential to intelligently coordinate the operations of the MGC.This paper presents a multi-time-scale EMS consid-ering battery operational modes for grid-connected MGCs.The proposed strategy consists of two modules:day-ahead integrated optimization and realtime distributed compensation.The first module aims to minimize the operational cost of the MGC considering battery free-overcharging protecting.This problem is solved by the mixed integer linear programming(MILP)sim-ulating two charging/discharging modes:limited-current mode and constant-voltage mode.The second module is installed in local MGs to correct the optimizing deviations of the day-ahead static scheduling,which are caused by predicting errors of renewable energy and loads.The main contribution of this work is integrating the advantages of global optimization of the centralized method and the fast computing speed of the distributed method.Experimental results prove the proposed EMS is feasible and effective.The computing time at each updating step is reduced by 75%on average,which has the potential to be adopted in engineering.

Micro-sonic sensor technology enables enhanced grass height measurement by a Rising Plate Meter

Globally,the Rising Plate Meter(RPM)is a device used to measure compressed sward height,to enable estimation of herbage mass.Despite improved farm management practices aided by a variety of technological advances,the standard design of a RPM has remained relatively unchanged.Recently,however,a RPM utilising a micro-sonic sensor,with digital data capture capability via a Bluetooth communications link to a smart device application,has been developed.Here,we assess the comparable ability of both a standard cumulative ratchet counter RPM and the micro-sonic sensor RPM,to accurately and precisely measure fixed heights.Moreover,as correct allocation of grazing area requires accurate geolocation positioning,we assess the associated GPS technology.The micro-sonic sensor RPM was significantly more accurate for height capture than the cumulative ratchet counter RPM.Overall,across all heights,the cumulative ratchet counter RPM underestimated height by 7.68±0.06mm(mean±SE).Alternatively,the micro-sonic sensor RPM overestimated height by 0.18±0.08 mm.In relation to a practical applications,these discrepancies can result in an under-and overestimation of dry matter yield by 13.71%and 0.32%kilograms per hectare,respectively.The performance of the on-board GPS did not significantly differ from that of a tertiary device.Overall,the wireless technology,integrated mapping,and decision support tools offered by the innovative micro-sonic sensor RPM provides for a highly efficacious grassland management tool.

Gripper Design for Radio Base Station Autonomous Maintenance System

For an autonomous system to perform maintenance tasks in a networking device or a radio base station(RBS), it has to deal with a series of technological challenges ranging from identifying hardware-related problems to manipulating connectors. This paper describes the development of a robot maintenance system dedicated to detect and resolve faulty links caused by unplugged or poorly connected cables. Although the maintenance system relies on four subsystems, we significantly focus on our low-cost and efficient custom gripper solution developed to handle RJ45 Ethernet connectors. To examine our gripper, we conducted three experiments. First, a qualitative questionnaire was submitted to 30 users in the case of the teleoperated scenario of the gripper attached to a robotic arm. Similarly, we also tested the automatic operation mode. The results showed that our system is reliable and delivers a highly efficient maintenance tool in both teleoperated and autonomous operation modes. The practical experiment containing the removal or unplugging of connectors demonstrated our gripper′s ability to adequately handle these, whereas the feedback from the questionnaire pointed to a positive user experience. The automatic test assessed the gripper′s robustness against the continuous operation.

Sustainable transit vehicle tracking service,using intelligent transportation system services and emerging communication technologies:A review

The most salient problems of transit vehicle service in Latin American intermediate cities include:the high number of passengers involved in traffic accidents;traffic congestion caused by transit vehicles,and pollution generated by these vehicles,which increases in high congestion scenarios.To improve upon this situation,a research was conducted on the transit vehicle tracking service,which is a basic service for implementing mobility solutions for the aforementioned problems,the most relevant characteristics of this service for the context of Latin American intermediate cities were identified,and an implementation was proposed.This paper presents the four stages of the study:(a)a review of the state-of-the-art of services or systems related to vehicle tracking,including wireless communications technologies,implemented sustainability approaches,usage of special algorithms for efficiency improvement,and the intelligent transportation system(ITS)architecture used as a basis;(b)the process of identifying relevant characteristics of the service for a given context;(c)proposal of an ITS architecture for this service in an intermediate city,its requirements and the suggested technologies;and(d)development of experiments for validating usage of the key suggested technologies.The review allowed to identify the main service characteristics,with regard to vehicle positioning technologies,the recommended wireless communication technology(long range,LoRa),energy consumption considerations,and use of artificial intelligence(AI)to calculate waiting time of users at bus stops.Finally,an ITS architecture for the city of Popayan(Colombian city)considering the aforementioned characteristics is proposed,and the experiments related to the use of these technologies are described in detail.

Intelligent collision risk detection in medium-sized cities of developing countries,using naturalistic driving:A review

Traffic accidents are one of the most serious problems worldwide,being one of the leading causes of death and economic loss in the world.Low-and middle-income countries,mainly their medium-sized cities,are among the most affected by this problem.93%of traffic accidents occur in low and middle-income countries,even though these countries have approximately 60%of the world’s vehicles.This occurs mainly because in these types of countries,especially in medium-sized cities(target context),there are no ideal conditions for driving,such as adequate road infrastructure,good condition of vehicles,and rigorous safety policies.Advanced data analysis techniques including machine learning(ML)have increasingly been used to solve this problem.Naturalistic driving(ND)can be applied as a data collection method that provides information on traffic accidents.ND commonly uses a vehicle’s kinematic data to detect high-risk driving behaviors that could cause an accident.The objectives of this document are to present a review of different alternatives that help in data collection and creation of intelligent solutions related to detection of possible traffic accidents,principally using ND;and to propose an intelligent collision risk detection system(ICRDS)for identification of areas with a high probability of TA in the target context.Through the review,it was possible to analyze and evaluate the devices,variables and algorithms that help characterize a risk event in driving,considering the target context.The development of a prototype of an ICRDS for a medium-sized city in a developing country is considered viable,considering the identified components,with the aim of identifying risk events in driving,and areas of high probability of accidents in the city.