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

Anthony D. Miyazaki, Ana Fernández

Joseph Bak-Coleman, Jevin West, Cailin O'Connor et al.

To what extent is social media research independent from industry influence? Leveraging openly available data, we show that half of the research published in top journals has disclosable ties to industry in the form of prior funding, collaboration, or employment. However, the majority of these ties go undisclosed in the published research. These trends do not arise from broad scientific engagement with industry, but rather from a select group of scientists who maintain long-lasting relationships with industry. Undisclosed ties to industry are common not just among authors, but among reviewers and academic editors during manuscript evaluation. Further, industry-tied research garners more attention within the academy, among policymakers, on social media, and in the news. Finally, we find evidence that industry ties are associated with a topical focus away from impacts of platform-scale features. Together, these findings suggest industry influence in social media research is extensive, impactful, and often opaque. Going forward there is a need to strengthen disclosure norms and implement policies to ensure the visibility of independent research, and the integrity of industry supported research.

Nurul Huda Mahmood, Onel L. A. Lopez, David Ruiz-Guirola et al.

Dependability - a system's ability to consistently provide reliable services by ensuring safety and maintainability in the face of internal or external disruptions - is a fundamental requirement for industrial wireless communication networks (IWCNs). While 5G ultra-reliable low-latency communication (URLLC) addresses some aspects of this challenge, its evolution toward holistic dependability in 6G must encompass reliability, availability, safety, and security. This paper provides a comprehensive framework for dependable IWCNs, bridging theory and practice. We first establish the theoretical foundations of dependability, including outlining its key attributes and presenting analytical tools to study it. Next, we explore practical enablers, such as adaptive multiple access schemes leveraging real-time monitoring and time-sensitive networking to ensure end-to-end determinism. A case study demonstrates how intelligent wake-up protocols improve event detection probability by orders of magnitude compared to conventional duty cycling. Finally, we outline open challenges and future directions for a 6G-driven dependable IWCN.

Ursula do C. Resende, Yan S. Gonçalves, Icaro V. Soares

This study investigates four rectenna system configurations using microstrip patch antennas with different geometries fabricated on various substrates, including glass, paper, fiberglass (FR4), and polyester fabric. A voltage doubler rectifier circuit was designed for each antenna type to enhance performance. Metamaterial cells were incorporated into the glass, FR4, and paper-based systems to improve electromagnetic wave capture. Simulations and optimizations were conducted using computer simulation technology (CST) and advanced design system (ADS) for operation at 2.45 GHz. Both the simulated and measured results that were obtained validate the potential of the proposed systems as efficient energy-harvesting solutions.

Henryk Fukś

For many cellular automata, it is possible to express the state of a given cell after $n$ iterations as an explicit function of the initial configuration. We say that for such rules the solution of the initial value problem can be obtained. In some cases, one can construct the solution formula for the initial value problem by analyzing the spatiotemporal pattern generated by the rule and decomposing it into simpler segments which one can then describe algebraically. We show an example of a rule when such approach is successful, namely elementary rule 156. Solution of the initial value problem for this rule is constructed and then used to compute the density of ones after $n$ iterations, starting from a random initial condition. We also show how to obtain probabilities of occurrence of longer blocks of symbols.

Michiel Rollier, Aisling J. Daly, Odemir M. Bruno et al.

Cellular automata (CAs) and convolutional neural networks (CNNs) are closely related due to the local nature of information processing. The connection between these topics is beneficial to both related fields, for conceptual as well as practical reasons. Our contribution solidifies this connection in the case of non-uniform CAs (nuCAs), simulating a global update in the architecture of the Python package TensorFlow. Additionally, we demonstrate how the highly optimised out-of-the-box multiprocessing in TensorFlow offers interesting computational benefits, especially when simulating large numbers of nuCAs with many cells.

Franco Bagnoli, Michele Baia, Tommaso Matteuzzi

We investigate elementary cellular automata (ECA) from the point of view of (discrete) dynamical systems. By studying small lattice sizes, we obtain the complete phase space of all minimal ECA, and, starting from a maximal entropy distribution (all configurations equiprobable), we show how the dynamics affects this distribution. We then investigate how a vanishing noise alters this phase space, connecting attractors and modifying the asymptotic probability distribution. What is interesting is that this modification not always goes in the sense of decreasing the entropy.

Bruno Santos, Rogério Luís C. Costa, Leonel Santos

Unlocking the potential of Industry 5.0 hinges on robust cybersecurity measures. This new Industrial Revolution prioritises human-centric values while addressing pressing societal issues such as resource conservation, climate change, and social stability. Recognising the heightened risk of cyberattacks due to the new enabling technologies in Industry 5.0, this paper analyses potential threats and corresponding countermeasures. Furthermore, it evaluates the existing industrial implementation frameworks, which reveals their inadequacy in ensuring a secure transition from Industry 4.0 to Industry 5.0. Consequently, the paper underscores the necessity of developing a new framework centred on cybersecurity to facilitate organisations' secure adoption of Industry 5.0 principles. The creation of such a framework is emphasised as a necessity for organisations.

Shen Wang, M. Delavar, M. A. Azad et al.

Caller ID spoofing is a global industry problem and often acts as a critical enabler for telephone fraud. To address this problem, the Federal Communications Commission has mandated telecom providers in the U.S. to implement STIR/SHAKEN, an industry-driven solution based on digital signatures. STIR/SHAKEN relies on a public key infrastructure (PKI) to manage digital certificates, but scaling up this PKI for the global telecom industry is extremely difficult, if not impossible. Furthermore, it only works with IP-based systems (e.g., SIP), leaving the traditional non-IP systems (e.g., SS7) unprotected. So far the alternatives to the STIR/SHAKEN have not been sufficiently studied. In this article, we propose a PKI-free solution, called Caller ID Verification (CIV). CIV authenticates the caller ID based on a challenge-response process instead of digital signatures, hence requiring no PKI. It supports both IP and non-IP systems. Perhaps counter-intuitively, we show that number spoofing can be leveraged, in conjunction with Dual-tone Multi-frequency, to efficiently implement the challenge-response process, i.e., using spoofing to fight against spoofing. We implement CIV for Voice over Internet Protocol, cellular, and landline phones across heterogeneous networks (SS7/SIP) by only updating the software on the user’s phone. This is the first caller ID authentication solution with working prototypes for all three types of telephone systems in the current telecom architecture. Finally, we show how the implementation of CIV can be optimized by integrating it into telecom clouds as a service, which users may subscribe to.

Maksim Papenkov, Chris Meredith, Claire Noel et al.

Accurate industry classification is a critical tool for many asset management applications. While the current industry gold-standard GICS (Global Industry Classification Standard) has proven to be reliable and robust in many settings, it has limitations that cannot be ignored. Fundamentally, GICS is a single-industry model, in which every firm is assigned to exactly one group - regardless of how diversified that firm may be. This approach breaks down for large conglomerates like Amazon, which have risk exposure spread out across multiple sectors. We attempt to overcome these limitations by developing MIS (Multi-Industry Simplex), a probabilistic model that can flexibly assign a firm to as many industries as can be supported by the data. In particular, we utilize topic modeling, an natural language processing approach that utilizes business descriptions to extract and identify corresponding industries. Each identified industry comes with a relevance probability, allowing for high interpretability and easy auditing, circumventing the black-box nature of alternative machine learning approaches. We describe this model in detail and provide two use-cases that are relevant to asset management - thematic portfolios and nearest neighbor identification. While our approach has limitations of its own, we demonstrate the viability of probabilistic industry classification and hope to inspire future research in this field.

Henryk Fukś

We show how to construct a deterministic nearest-neighbour cellular automaton (CA) with four states which emulates diffusion on a one-dimensional lattice. The pseudo-random numbers needed for directing random walkers in the diffusion process are generated with the help of rule 30. This CA produces density profiles which agree very well with solutions of the diffusion equation, and we discuss this agreement for two different boundary and initial conditions. We also show how our construction can be generalized to higher dimensions.

Pengwei Yang, Amani Abusafia, Abdallah Lakhdari et al.

We propose a novel Energy Loss Prediction(ELP) framework that estimates the energy loss in sharing crowdsourced energy services. Crowdsourcing wireless energy services is a novel and convenient solution to enable the ubiquitous charging of nearby IoT devices. Therefore, capturing the wireless energy sharing loss is essential for the successful deployment of efficient energy service composition techniques. We propose Easeformer, a novel attention-based algorithm to predict the battery levels of IoT devices in a crowdsourced energy sharing environment. The predicted battery levels are used to estimate the energy loss. A set of experiments were conducted to demonstrate the feasibility and effectiveness of the proposed framework. We conducted extensive experiments on real wireless energy datasets to demonstrate that our framework significantly outperforms existing methods.

P. Priyadarshinee, Rakesh D. Raut, M. Jha et al.

S. Deery, Roderick D. Iverson, Janet Walsh

Fardin Ghorbani, Hossein Soleimani

The purpose of this paper is to present a deep learning model that simultaneously estimates targets and wall parameters in through-the-wall radar (TWR). As a result of the complexity of the environments in which through-the-wall radars operate, TWR faces many challenges. The propagation of radar signals through walls is further delayed and attenuated than in free space. Therefore, the targets are less able to be detected and the images of the targets are distorted and defocused as a consequence. To address the above challenges, two modes are considered in this work: single targets and two targets. In both cases, permittivity and wall thickness are considered, along with the target’s center in two dimensions and the permittivity of targets. Therefore, in the case of a single target, we estimate five values, whereas in the case of two targets, we estimate eight values simultaneously, each representing the mentioned parameters. As a result of using deep neural networks to solve the task of target locating problem in TWR, the model has a better chance of learning and increased accuracy if it involves more parameters (such as wall parameters and permittivity of the wall) in the target location problem. In this way, the accuracy of target locating improved when two wall parameters were considered in problem. A deep neural network model was used to estimate wall permittivity and thickness, as well as two-dimensional coordinates and permittivity of targets with 99% accuracy in single-target and two-target modes.

Yuanyuan Tian

Rapidly growing social networks and other graph data have created a high demand for graph technologies in the market. A plethora of graph databases, systems, and solutions have emerged, as a result. On the other hand, graph has long been a well studied area in the database research community. Despite the numerous surveys on various graph research topics, there is a lack of survey on graph technologies from an industry perspective. The purpose of this paper is to provide the research community with an industrial perspective on the graph database landscape, so that graph researcher can better understand the industry trend and the challenges that the industry is facing, and work on solutions to help address these problems.

L. Linnan, M. Bowling, Jennifer Childress et al.

Aiting Wu, Furan Zhu, Pengquan Zhang et al.

This paper proposes a self-shape blending algorithm to improve antenna bandwidth. A printed antenna is designed for bandwidth enhancement based on the proposed algorithm; this approach can also be used to enhance bandwidth in other applications. The antenna completely covers WLAN bands and WiMAX bands after the proposed algorithm is applied. The shape of the rotating slot and the parasitic patch also changes, which excites additional resonance and improves the impedance matching at high frequencies. Test results show that the proposed antenna can work from 2.07 GHz to 5.94 GHz with S11≤−10dB. Compared to a slot antenna without the self-shape blending algorithm, the bandwidth increases by more than 0.7 GHz.

Chun-Hong Chen, Pei-Yang Wang, Jun Chen et al.

A single-layer capsule-shaped polarization conversion metasurface (PCM) is proposed in this paper. In the W-band, its polarization conversion rate (PCR) exceeds 97%, effectively changing the polarization direction of the incident wave. PCM is arranged in a chessboard array to achieve broadband RCS reduction. Placing the PCM array on a circularly polarized sequentially rotated slot antenna array, simulated results show that the radiation characteristics of the antenna array are hardly affected by the PCM array. The results of measurement demonstrate that the RCS of the antenna array with PCM array proposed is reduced by more than 10 dB from 40 to 119 GHz; the relative bandwidth (−10 dB) reaches 96.3%.

Tiago Espinha Gasiba, Ulrike Lechner, Maria Pinto-Albuquerque

Awareness of cybersecurity topics, e.g., related to secure coding guidelines, enables software developers to write secure code. This awareness is vital in industrial environments for the products and services in critical infrastructures. In this work, we introduce and discuss a new serious game designed for software developers in the industry. This game addresses software developers' needs and is shown to be well suited for raising secure coding awareness of software developers in the industry. Our work results from the experience of the authors gained in conducting more than ten CyberSecurity Challenges in the industry. The presented game design, which is shown to be well accepted by software developers, is a novel alternative to traditional classroom training. We hope to make a positive impact in the industry by improving the cybersecurity of products at their early production stages.