Hasil untuk "Cellular telephone services industry. Wireless telephone industry"

Dhruv Dosi, Rohit Meena, Param Rajpura et al.

Legacy floor plans, often preserved only as scanned documents, remain essential resources for architecture, urban planning, and facility management in the construction industry. However, the lack of machine-readable floor plans render large-scale interpretation both time-consuming and error-prone. Automated symbol spotting offers a scalable solution by enabling the identification of service key symbols directly from floor plans, supporting workflows such as cost estimation, infrastructure maintenance, and regulatory compliance. This work introduces a labelled Digitised Electrical Layout Plans (DELP) dataset comprising 45 scanned electrical layout plans annotated with 2,450 instances across 34 distinct service key classes. A systematic evaluation framework is proposed using pretrained object detection models for DELP dataset. Among the models benchmarked, YOLOv8 achieves the highest performance with a mean Average Precision (mAP) of 82.5\%. Using YOLOv8, we develop SkeySpot, a lightweight, open-source toolkit for real-time detection, classification, and quantification of electrical symbols. SkeySpot produces structured, standardised outputs that can be scaled up for interoperable building information workflows, ultimately enabling compatibility across downstream applications and regulatory platforms. By lowering dependency on proprietary CAD systems and reducing manual annotation effort, this approach makes the digitisation of electrical layouts more accessible to small and medium-sized enterprises (SMEs) in the construction industry, while supporting broader goals of standardisation, interoperability, and sustainability in the built environment.

Suguru Otani

I investigate how explicit cartels, known as ``shipping conferences", in a global container shipping market facilitated the formation of one of the largest globally integrated markets through entry, exit, and shipbuilding investment of shipping firms. Using a novel data, I develop and construct a structural model and find that the cartels shifted shipping prices by 20-50\% and encouraged firms' entry and investment. In the counterfactual, I find that cartels would increase producer surplus while slightly decreasing consumer surplus, then may increase social welfare by encouraging firms' entry and shipbuilding investment. This would validate industry policies controlling prices and quantities in the early stage of the new industry, which may not be always harmful. Investigating hypothetical allocation rules supporting large or small firms, I find that the actual rule based on tonnage shares is the best to maximize social welfare.

David Wichner, Jeffrey Wishart, Jason Sergent et al.

Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV industry based on robust taxonomy development and validation criteria and provides rationale for such an approach. Keywords: Safety Management System (SMS), Automated Driving System (ADS), ADS-Equipped Vehicle, Autonomous Vehicles (AV)

Fernando Miguelez, Josu Doncel, Maria Dolores Ugarte

Industrial processes generate a massive amount of monitoring data that can be exploited to uncover hidden time losses in the system. This can be used to enhance the accuracy of maintenance policies and increase the effectiveness of the equipment. In this work, we propose a method for one-step probabilistic multivariate forecasting of time variables involved in a production process. The method is based on an Input-Output Hidden Markov Model (IO-HMM), in which the parameters of interest are the state transition probabilities and the parameters of the observations' joint density. The ultimate goal of the method is to predict operational process times in the near future, which enables the identification of hidden losses and the location of improvement areas in the process. The input stream in the IO-HMM model includes past values of the response variables and other process features, such as calendar variables, that can have an impact on the model's parameters. The discrete part of the IO-HMM models the operational mode of the process. The state transition probabilities are supposed to change over time and are updated using Bayesian principles. The continuous part of the IO-HMM models the joint density of the response variables. The estimate of the continuous model parameters is recursively computed through an adaptive algorithm that also admits a Bayesian interpretation. The adaptive algorithm allows for efficient updating of the current parameter estimates as soon as new information is available. We evaluate the method's performance using a real data set obtained from a company in a particular sector, and the results are compared with a collection of benchmark models.

Rasha Kashef, Monika Freunek, Jeff Schwartzentruber et al.

There is a perceived disconnect between how ad hoc industry solutions and academic research solutions in cyber security are developed and applied. Is there a difference in philosophy in how solutions to cyber security problems are developed by industry and by academia. What could academia and industry do to bridge this gap and speed up the development and use of effective cybersecurity solutions? This paper provides an overview of the most critical gaps and solutions identified by an interdisciplinary expert exchange on the topic. The discussion was held in the form of the webinar "Bridging the Bubbles: Connecting Academia and Industry in Cybersecurity Research" in November 2022 as part of the Rogers Cybersecure Catalyst webinar series. Panelists included researchers from academia and industry as well as experts from industry and business development. The key findings and recommendations of this exchange are supported by the relevant scientific literature on the topic within this paper. Different approaches and time frames in development and lifecycle management, challenges in knowledge transfer and communication as well as heterogeneous metrics for success in projects are examples of the evaluated subject areas.

Petr Kostka, Bruno Rossi, Mouzhi Ge

The fourth industrial revolution and the digital transformation, commonly known as Industry 4.0, is exponentially progressing in recent years. Connected computers, devices, and intelligent machines communicate with each other and interact with the environment to require only a minimum of human intervention. An important issue in Industry 4.0 is the evaluation of the quality of the process in terms of KPIs. Monte Carlo simulations can play an important role to improve the estimations. However, there is still a lack of clear workflow to conduct the Monte Carlo simulations for selecting different Monte Carlo methods. This paper, therefore, proposes a simulation flow for conducting Monte Carlo methods comparison in Industry 4.0 applications. Based on the simulation flow, we compare Cumulative Monte Carlo and Markov Chain Monte Carlo methods. The experimental results show the way to use the Monte Carlo methods in Industry 4.0 and possible limitations of the two simulation methods.

Franco D. Albareti, Thomas Ankenbrand, Denis Bieri et al.

Quantum computers can solve specific problems that are not feasible on "classical" hardware. Harvesting the speed-up provided by quantum computers therefore has the potential to change any industry which uses computation, including finance. First quantum applications for the financial industry involving optimization, simulation, and machine learning problems have already been proposed and applied to use cases such as portfolio management, risk management, and pricing derivatives. This survey reviews platforms, algorithms, methodologies, and use cases of quantum computing for various applications in finance in a structured way. It is aimed at people working in the financial industry and serves to gain an overview of the current development and capabilities and understand the potential of quantum computing in the financial industry.

Chao Liu, Feiyang Zhou

In this article, we proposed a multiparameter joint estimation algorithm based on the dual-polarized cylindrical conformal array (DCCA) in the presence of mutual coupling. Using the characteristic of the dual-polarized cylindrical conformal array, 2D direction-of-arrival (DOA) estimation can be divided into 1D estimations of elevation and azimuth. Sensors on the boundary of the DCCA are set as auxiliary sensors to eliminate the influence of mutual coupling. Then, elevation can be estimated by the generalized eigenvalues utilizing signal subspace eigenvectors (GEESE). After that, polarization sensitivity can be eliminated by projection transformation and the proposed dual-polarized forward-backward smoothing algorithm. Consequently, a dual-polarized spatial spectrum can be developed to estimate the azimuth based on the estimated elevation. Furthermore, the angles of the signals can be reestimated to improve the accuracy of DOA estimation. Simulation results confirm the effectiveness of the proposed algorithm.

Shah Zeb, Aamir Mahmood, Syed Ali Hassan et al.

By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry~4.0 promotes integrating cyber-physical worlds through cyber-physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT is an emerging but conceptually different construct than CPS. Like CPS, DT relies on communication to create a highly-consistent, synchronized digital mirror image of the objects or physical processes. DT, in addition, uses built-in models on this precise image to simulate, analyze, predict, and optimize their real-time operation using feedback. DT is rapidly diffusing in the industries with recent advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and requirements of these technologies in DT-enabled industries from the communication and computing perspective. In this article, we first present the functional aspects, appeal, and innovative use of DT in smart industries. Then, we elaborate on this perspective by systematically reviewing and reflecting on recent research in next-generation (NextG) wireless technologies (e.g., 5G and beyond networks), various tools (e.g., age of information, federated learning, data analytics), and other promising trends in networked computing (e.g., edge and cloud computing). Moreover, we discuss the DT deployment strategies at different industrial communication layers to meet the monitoring and control requirements of industrial applications. We also outline several key reflections and future research challenges and directions to facilitate industrial DT's adoption.

Hadi Alasti

This paper presents an experimental adaptation of a non-collaborative robot arm to collaborate with the environment, as one step towards adapting legacy robotic machinery to fit in industry 4.0 requirements. A cloud-based internet of things (CIoT) service is employed to connect, supervise and control a robotic arm's motion using the added wireless sensing devices to the environment. A programmable automation controller (PAC) unit, connected to the robot arm receives the most recent changes and updates the motion of the robot arm. The experimental results show that the proposed non-expensive service is tractable and adaptable to higher level for machine to machine collaboration. The proposed approach in this paper has industrial and educational applications. In the proposed approach, the CIoT technology is added as a technology interface between the sensors to the environment and the robotic arm. The proposed approach is versatile and fits to variety of applications to meet the flexible requirements of industry 4.0. The proposed approach has been implemented in an experiment using MECA 500 robot arm and AMAX 5580 programmable automation controller and ultrasonic proximity wireless sensor.

Bin Han, Hans D. Schotten

The next revolution of industry will turn the industries as well as the entire society into a human-centric shape. The human presence in industrial environment and the human participation in industrial processes will be magnified more than ever before. To cope with the emerging challenges raised by this revolution, 6G ambitions to bridge the three domains of digital information, physical assets and humans into one merged cyber-physical-human world. This proposes not only an unprecedented demand for digital twin solutions, but also new technical requirements. Especially, aiming at a human-centric industrial DT system, novel multi-sensory human-machine interfaces will play a key role in this paradigm shift.

Leonardo Bonati, Salvatore D'Oro, Francesco Restuccia et al.

Fifth-generation (5G) systems will extensively employ radio access network (RAN) softwarization. This key innovation enables the instantiation of "virtual cellular networks" running on different slices of the shared physical infrastructure. In this paper, we propose the concept of Private Cellular Connectivity as a Service (PCCaaS), where infrastructure providers deploy covert network slices known only to a subset of users. We then present SteaLTE as the first realization of a PCCaaS-enabling system for cellular networks. At its core, SteaLTE utilizes wireless steganography to disguise data as noise to adversarial receivers. Differently from previous work, however, it takes a full-stack approach to steganography, contributing an LTE-compliant steganographic protocol stack for PCCaaS-based communications, and packet schedulers and operations to embed covert data streams on top of traditional cellular traffic (primary traffic). SteaLTE balances undetectability and performance by mimicking channel impairments so that covert data waveforms are almost indistinguishable from noise. We evaluate the performance of SteaLTE on an indoor LTE-compliant testbed under different traffic profiles, distance and mobility patterns. We further test it on the outdoor PAWR POWDER platform over long-range cellular links. Results show that in most experiments SteaLTE imposes little loss of primary traffic throughput in presence of covert data transmissions (< 6%), making it suitable for undetectable PCCaaS networking.

Abdallah Lakhdari, Athman Bouguettaya

We propose a novel fluid composition approach of wireless energy services in a crowdsourced IoT environment. The proposed approach selects an optimal set of dynamic energy services according to the consumer's requirements. We leverage the mobility patterns of the crowd in confined areas to capture the intermittent behavior of IoT energy services. We model the IoT energy services based on their mobility patterns to propose a knapsack-based heuristic for the fluid composition. Experimental results demonstrate the efficiency of the proposed approach.

Babar Shahzaad, Athman Bouguettaya, Sajib Mistry

We propose a novel game-theoretic approach for drone service composition considering recharging constraints. We design a non-cooperative game model for drone services. We propose a non-cooperative game algorithm for the selection and composition of optimal drone services. We conduct several experiments on a real drone dataset to demonstrate the efficiency of our proposed approach.

Maedeh Sharifinejad, Ali Dorri, Javad Rezazadeh

Insurance is one of the fundamental services offered to the citizens to reduce their costs and assist them in case of an emergency. One of the most important challenges in the insurance industry is to address liability challenge and the forging of documents by the involved parties, i.e., insurance company or the users, in order to increase financial gain. Conventional methods to address this challenge is significantly time consuming and costly and also suffers from lock of transparency. In this paper, we propose a blockchain-based solution for the insurance industry in smart cities (BIS). BIS creates a big umbrella that consists of the smart city managers, insurance companies, users, and sensors and devices. The users are known by changeable Public Keys (PKs) that introduces a level of anonymity. The data collected by the sensors is stored in cloud or local storage and is shared with insurance company on demand to find the liable party that in turn increases the privacy of the users. BIS enables the users to prove and share the history of their insurances with other users or insurances. Using Proof of Concept (POC) implementation we demonstrated the applicability of blockchain in insurance industry. The implementation results prove that BIS significantly reduces delay involved in insurance industry as compared with conventional insurance methods.

Zhirong An, Mang He

A differentially fed dual-polarized antenna with low cross-polarization is proposed for sub-6 GHz applications. The main patch is fed through two pairs of symmetrical ports, and annular-ring slits are etched around the feedings. The broadband 180° phase shifter provides a stable differential feeding structure, and a 1 mm thick radome with a parasitic patch printed on its inner surface is utilized to expand the impedance bandwidth. The impedance bandwidth of the proposed antenna ranges from 3.3 to 6.0 GHz, covering the entire sub-6 GHz band. The 4-element antenna array features low profile, wide bandwidth, low cross-polarization level, and stable gain over the entire operating band. The prototype of the antenna array is fabricated and measured, and the design is well validated by experimental results.

Ming Li, Haiping Wei, Jiahao Zhao et al.

In this paper, on the basis of multifunctional reconfigurable pixel antenna (RPA) elements, a novel linear sparse array with an attractive compound reconfigurability is presented. It has the potential advantages of its beam scanning with low gain fluctuation, low sidelobe in two orthogonal planes, and polarization reconfigurable performance. Specifically, an RPA with simultaneous polarization and pattern reconstruction capabilities, consisting of the driven patch and the parasitic pixels on the same layer of dielectric substrate, is firstly designed, which can work in several operation modes corresponding to steerable beam directions θ=0°;θxoz=25°, 45°;θyoz=15° with two circular polarizations in X-band. Cross-slot coupling feed is used to improve polarization reconstruction capability and reduce the complexity of hybrid reconstruction topology optimization. Then, those RPAs are integrated into the 1×8 linear sparse array to realize the reconfiguration of two circular polarizations and beam steering in xoz- and yoz-plane. Simulation results show that the gain fluctuation and sidelobe level of the array during beam scanning have significant advantages over the previous phased array, and the generation of antenna grating lobes is avoided. Moreover, both RPA element and RPA array prototypes have been fabricated and measured to testify the efficiency. The measured results agree well with the simulated ones, which indicates the application potential in the field of modern wireless communication system of the proposed linear sparse array.

Samuele Zoppi, Touraj Soleymani, Markus Klügel et al.

Novel low-power wireless technologies and IoT applications open the door to the Industrial Internet of Things (IIoT). In this new paradigm, Wireless Sensor Networks (WSNs) must fulfil, despite energy and transmission power limitations, the challenging communication requirements of advanced manufacturing processes and technologies. In industrial networks, this is possible thanks to the availability of network infrastructure and the presence of a network coordinator that efficiently allocates the available radio resources. In this work, we consider a WSN that simultaneously transmits measurements of Networked Control Systems' (NCSs) dynamics to remote state estimators over a shared packet-erasure channel. We develop a minimum transmission power control (TPC) policy for the coordination of the wireless medium by formulating an infinite horizon Markov decision process (MDP) optimization problem. We compute the policy using an approximate value iteration algorithm and provide an extensive evaluation of its parameters in different interference scenarios and NCSs dynamics. The evaluation results present a comprehensive characterization of the algorithm's performance, proving that it can flexibly adapt to arbitrary use cases.

Adnan Aijaz, Aleksandar Stanoev, Mahesh Sooriyabandara

The use of mobile platforms (MPs) is particularly attractive for various industrial applications. This demonstration highlights the importance of remote control of MPs and shows its viability over a high-performance wireless solution designed for closed-loop control. Further, it shows the viability of formation control of a network of MPs through a leader-follower approach underpinned by high-performance wireless.

Zhiyuan Jiang, Siyu Fu, Sheng Zhou et al.

The 5G Phase-2 and beyond wireless systems will focus more on vertical applications such as autonomous driving and industrial Internet-of-things, many of which are categorized as ultra-Reliable Low-Latency Communications (uRLLC). In this article, an alternative view on uRLLC is presented, that information latency, which measures the distortion of information resulted from time lag of its acquisition process, is more relevant than conventional communication latency of uRLLC in wireless networked control systems. An AI-assisted Situationally-aware Multi-Agent Reinforcement learning framework for wireless neTworks (SMART) is presented to address the information latency optimization challenge. Case studies of typical applications in Autonomous Driving (AD) are demonstrated, i.e., dense platooning and intersection management, which show that SMART can effectively optimize information latency, and more importantly, information latency-optimized systems outperform conventional uRLLC-oriented systems significantly in terms of AD performance such as traffic efficiency, thus pointing out a new research and system design paradigm.