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

K. Letaief, Wei Chen, Yuanming Shi et al.

The recent upsurge of diversified mobile applications, especially those supported by Artificial Intelligence (AI), is spurring heated discussions on the future evolution of wireless communications. While 5G is being deployed around the world, efforts from industry and academia have started to look beyond 5G and conceptualize 6G. We envision 6G to undergo an unprecedented transformation that will make it substantially different from the previous generations of wireless cellular systems. In particular, 6G will go beyond mobile Internet and will be required to support ubiquitous AI services from the core to the end devices of the network. Meanwhile, AI will play a critical role in designing and optimizing 6G architectures, protocols, and operations. In this article, we discuss potential technologies for 6G to enable mobile AI applications, as well as AI-enabled methodologies for 6G network design and optimization. Key trends in the evolution to 6G will also be discussed.

Jingzhi Gong, Rafail Giavrimis, Paul Brookes et al.

There is a growing interest in leveraging multiple large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering. This cross-model prompt engineering bottleneck severely limits the practical deployment of multi-LLM systems in production environments. We introduce Meta-Prompted Code Optimization (MPCO), a framework that automatically generates high-quality, task-specific prompts across diverse LLMs while maintaining industrial efficiency requirements. MPCO leverages metaprompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts. It is an essential part of the ARTEMIS code optimization platform for automated validation and scaling. Our comprehensive evaluation on five real-world codebases with 366 hours of runtime benchmarking demonstrates MPCO's effectiveness: it achieves overall performance improvements up to 19.06% with the best statistical rank across all systems compared to baseline methods. Analysis shows that 96% of the top-performing optimizations stem from meaningful edits. Through systematic ablation studies and meta-prompter sensitivity analysis, we identify that comprehensive context integration is essential for effective meta-prompting and that major LLMs can serve effectively as meta-prompters, providing actionable insights for industrial practitioners.

Sithembele Nkonkobe

In this study we revisit the telephone exchange problem. We discuss a generalization of the telephone exchange problem by discuss two generalizations of the Bessel polynomials. We study combinatorial properties of these polynomials, and show how the numbers are related to the well known Whitney numbers and Dowling numbers

Qinwen Chen, Wenbiao Tao, Zhiwei Zhu et al.

Community Question Answering (CQA) platforms can be deemed as important knowledge bases in community, but effectively leveraging historical interactions and domain knowledge in real-time remains a challenge. Existing methods often underutilize external knowledge, fail to incorporate dynamic historical QA context, or lack memory mechanisms suited for industrial deployment. We propose ComRAG, a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism designed for retrieval, generation, and efficient storage. Evaluated on three industrial CQA datasets, ComRAG consistently outperforms all baselines--achieving up to 25.9% improvement in vector similarity, reducing latency by 8.7% to 23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations.

Max M. Lang, Sol Eskenazi

Telephone surveys remain a valuable tool for gathering insights but typically require substantial resources in training and coordinating human interviewers. This work presents an AI-driven telephone survey system integrating text-to-speech (TTS), a large language model (LLM), and speech-to-text (STT) that mimics the versatility of human-led interviews (full-duplex dialogues) at scale. We tested the system across two populations, a pilot study in the United States (n = 75) and a large-scale deployment in Peru (n = 2,739), inviting participants via web-based links and contacting them via direct phone calls. The AI agent successfully administered open-ended and closed-ended questions, handled basic clarifications, and dynamically navigated branching logic, allowing fast large-scale survey deployment without interviewer recruitment or training. Our findings demonstrate that while the AI system's probing for qualitative depth was more limited than human interviewers, overall data quality approached human-led standards for structured items. This study represents one of the first successful large-scale deployments of an LLM-based telephone interviewer in a real-world survey context. The AI-powered telephone survey system has the potential for expanding scalable, consistent data collecting across market research, social science, and public opinion studies, thus improving operational efficiency while maintaining appropriate data quality for research.

Andreas Mershin, Nikolas Stefanou, Adan Rotteveel et al.

Machine olfaction is rapidly emerging as a transformative capability, with applications spanning non-invasive medical diagnostics, industrial monitoring, agriculture, and security and defense. Recent advances in stabilizing mammalian olfactory receptors and integrating them into biophotonic and bioelectronic systems have enabled detection at near single-molecule resolution thus placing machines on par with trained detection dogs. As this technology converges with multimodal AI and distributed sensor networks imbued with embedded AI, it introduces a new, biochemical layer to a sensing ecosystem currently dominated by machine vision and audition. This review and industry roadmap surveys the scientific foundations, technological frontiers, and strategic applications of machine olfaction making the case that we are currently witnessing the rise of a new industry that brings with it a global chemosensory infrastructure. We cover exemplary industrial, military and consumer applications and address some of the ethical and legal concerns arising. We find that machine olfaction is poised to bring forth a planet-wide molecular awareness tech layer with the potential of spawning vast emerging markets in health, security, and environmental sensing via scent.

Zhijun Cai, Lingzhi Ren, Shuangbing Liu et al.

In this paper, we propose an improved adaptive frequency sweep strategy based on the asymptotic waveform evaluation (AWE) technique for simulating broadband antennas across a wide frequency range. Our approach maintains high accuracy compared to traditional frequency sweep methods, as demonstrated through simulation results of several commonly used broadband antennas. A novel error function is introduced specifically designed for broadband antenna simulation, which effectively improves the adaptive frequency sweep process. By analyzing the relationship between target error and computational time, we determine the optimal balance zone between efficiency and accuracy. Our findings provide valuable insights for efficient and accurate simulation of broadband antennas.

Lifeng Lin, Zijian Zhou, Peihe Jiang et al.

This paper presents a secure wire-line telephone system that employs physical layer security (PLS) to protect against wiretapping. The system generates artificial noise (AN) in both transmission directions and uses a telephone hybrid circuit to effectively suppress the AN for the purpose of secure communication. Furthermore, we analyze the secrecy capacity of the system and evaluate its performance through theoretical analysis and practical experiments. The results demonstrate that the proposed system can significantly enhance communication security while preserving the integrity of legitimate signals. The results also validate that the proposed system is a robust and effective solution for securing wire-line telephone communications.

Víctor Julio Ramírez-Durán, Idoia Berges, Arantza Illarramendi

Many different worldwide initiatives are promoting the transformation from machine dominant manufacturing to digital manufacturing. Thus, to achieve a successful transformation to Industry 4.0 standard, manufacturing enterprises are required to implement a clear roadmap. However, Small and Medium Manufacturing Enterprises (SMEs) encounter many barriers and difficulties (economical, technical, cultural, etc.) in the implementation of Industry 4.0. Although several works deal with the incorporation of Industry 4.0 technologies in the area of the product and supply chain life cycles, which SMEs could use as reference, this is not the case for the customer life cycle. Thus, we present two contributions that can help the software engineers of those SMEs to incorporate Industry 4.0 technologies in the context of the customer life cycle. The first contribution is a methodology that can help those software engineers in the task of creating new software services, aligned with Industry 4.0, that allow to change how customers interact with enterprises and the experiences they have while interacting with them. The methodology details a set of stages that are divided into phases which in turn are made up of activities. It places special emphasis on the incorporation of semantics descriptions and 3D visualization in the implementation of those new services. The second contribution is a system developed for a real manufacturing scenario, using the proposed methodology, which allows to observe the possibilities that this kind of systems can offer to SMEs in two phases of the customer life cycle: Discover & Shop, and Use & Service.

Emelyne Michel, Richard Philippe, Quentin Berdal

Remote control of trains will be an intermediary step before reaching full automation. In trains, use cases for remote control have been studied only for the past few years. This research presents a project about remote control for the next generation of trains in France and how we carry out the design of a new teleoperation desk for future remote train drivers. We present an Ergonomic Work Analysis used to precisely understand driver's activity. This analysis allowed us to identify the needs of future drivers and to propose ways to overcome one of the main problems that drivers will face when remotely driving a train: loss and degradation of sense. We explain how innovative technologies developed within the Industry 4.0 can offer solutions to problems faced with remote-control.

Paula Fraga-Lamas, Tiago M Fernandez-Carames, Oscar Blanco-Novoa et al.

Shipbuilding companies are upgrading their inner workings in order to create Shipyards 4.0, where the principles of Industry 4.0 are paving the way to further digitalized and optimized processes in an integrated network. Among the different Industry 4.0 technologies, this article focuses on Augmented Reality, whose application in the industrial field has led to the concept of Industrial Augmented Reality (IAR). This article first describes the basics of IAR and then carries out a thorough analysis of the latest IAR systems for industrial and shipbuilding applications. Then, in order to build a practical IAR system for shipyard workers, the main hardware and software solutions are compared. Finally, as a conclusion after reviewing all the aspects related to IAR for shipbuilding, it is proposed an IAR system architecture that combines Cloudlets and Fog Computing, which reduce latency response and accelerate rendering tasks while offloading compute intensive tasks from the Cloud.

Idoia Berges, Víctor Julio Ramírez-Durán, Arantza Illarramendi

The growing trends in automation, Internet of Things, big data and cloud computing technologies have led to the fourth industrial revolution (Industry 4.0), where it is possible to visualize and identify patterns and insights, which results in a better understanding of the data and can improve the manufacturing process. However, many times, the task of data exploration results difficult for manufacturing experts because they might be interested in analyzing also data that does not appear in pre-designed visualizations and therefore they must be assisted by Information Technology experts. In this paper, we present a proposal materialized in a semantic-based visual query system developed for a real Industry 4.0 scenario that allows domain experts to explore and visualize data in a friendly way. The main novelty of the system is the combined use that it makes of captured data that are semantically annotated first, and a 2D customized digital representation of a machine that is also linked with semantic descriptions. Those descriptions are expressed using terms of an ontology, where, among others, the sensors that are used to capture indicators about the performance of a machine that belongs to a Industry 4.0 scenario have been modeled. Moreover, this semantic description allows to: formulate queries at a higher level of abstraction, provide customized graphical visualizations of the results based on the format and nature of the data, and download enriched data enabling further types of analysis.

Zilong Zhang, Zhibin Zhao, Xingwu Zhang et al.

Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. The diversity of the datasets is the foundation for developing comprehensive IAD algorithms. Existing IAD datasets focus on the diversity of data categories, overlooking the diversity of domains within the same data category. In this paper, to bridge this gap, we propose the Aero-engine Blade Anomaly Detection (AeBAD) dataset, consisting of two sub-datasets: the single-blade dataset and the video anomaly detection dataset of blades. Compared to existing datasets, AeBAD has the following two characteristics: 1.) The target samples are not aligned and at different scales. 2.) There is a domain shift between the distribution of normal samples in the test set and the training set, where the domain shifts are mainly caused by the changes in illumination and view. Based on this dataset, we observe that current state-of-the-art (SOTA) IAD methods exhibit limitations when the domain of normal samples in the test set undergoes a shift. To address this issue, we propose a novel method called masked multi-scale reconstruction (MMR), which enhances the model's capacity to deduce causality among patches in normal samples by a masked reconstruction task. MMR achieves superior performance compared to SOTA methods on the AeBAD dataset. Furthermore, MMR achieves competitive performance with SOTA methods to detect the anomalies of different types on the MVTec AD dataset. Code and dataset are available at https://github.com/zhangzilongc/MMR.

J. Schüz, K. Pirie, G. Reeves et al.

Abstract Background The ongoing debate of whether use of cellular telephones increases the risk of developing a brain tumor was recently fueled by the launch of the fifth generation of wireless technologies. Here, we update follow-up of a large-scale prospective study on the association between cellular telephone use and brain tumors. Methods During 1996-2001, 1.3 million women born in 1935-1950 were recruited into the study. Questions on cellular telephone use were first asked in median year 2001 and again in median year 2011. All study participants were followed via record linkage to National Health Services databases on deaths and cancer registrations (including nonmalignant brain tumors). Results During 14 years follow-up of 776 156 women who completed the 2001 questionnaire, a total of 3268 incident brain tumors were registered. Adjusted relative risks for ever vs never cellular telephone use were 0.97 (95% confidence interval = 0.90 to 1.04) for all brain tumors, 0.89 (95% confidence interval = 0.80 to 0.99) for glioma, and not statistically significantly different to 1.0 for meningioma, pituitary tumors, and acoustic neuroma. Compared with never-users, no statistically significant associations were found, overall or by tumor subtype, for daily cellular telephone use or for having used cellular telephones for at least 10 years. Taking use in 2011 as baseline, there were no statistically significant associations with talking for at least 20 minutes per week or with at least 10 years use. For gliomas occurring in the temporal and parietal lobes, the parts of the brain most likely to be exposed to radiofrequency electromagnetic fields from cellular telephones, relative risks were slightly below 1.0. Conclusion Our findings support the accumulating evidence that cellular telephone use under usual conditions does not increase brain tumor incidence.

Zizu Chen, Guoliang Yu, Di Wu et al.

In this paper, a single-fed wideband circularly polarized dielectric resonator antenna (DRA) is proposed. The antenna consists of a substrate, a cylindrical dielectric resonator, and four curved parasitic metal blocks that are rotated and placed around the dielectric resonator. The GND of the substrate is etched with an inverse f-shaped slot. The orthogonal working mode of the DRA is excited by the inverse f-shaped slot coupled to the microstrip, thereby obtaining circularly polarized radiation. The impedance bandwidth and axial ratio bandwidth of the antenna are improved by optimizing the structure of the inverse f-shaped slot and the shape and size of curved parasitic metal blocks, and finally, a circularly polarized dielectric resonator antenna with significant bandwidth advantages is obtained. The antenna is simulated, fabricated, and measured. The measured results show that the proposed circularly polarized DRA has an impedance bandwidth of 58.5% (2.61–4.77 GHz) and an axial bandwidth of 55.2% (2.75–4.85 GHz) and finally achieves an effective axial bandwidth of 53.7% (2.75–4.77 GHz), the peak gain of 6.84 dBi, and average efficiency of 90.78%, with excellent overall performance.

Xin Huang, Yuming Wang, Yazhou Chen

Aiming at the problem that satellite navigation signals are easily interfered by the radio frequency (RF) pulse signal, the electromagnetic interference effect of RF pulse on the navigation receiver is studied in this paper. A mathematical model of the pulse interference signal is established, and we choose the bit error rate (BER) as an indicator of the quality of the BDS signal. It is found that the BER is proportional to the duty cycle of the pulse signal and inversely proportional to the equivalent carrier-to-noise ratio (C/N0) through simulation. Then, an experiment of electromagnetic injection on the BDS receiver has been carried out, which studied the influence of the pulse interference parameters, such as repetition frequency and duty cycle on the C/N0 of the BDS signal and the electromagnetic sensitivity threshold of the receiver. The comparative experiment between the pulse interference and the single-frequency continuous wave (CW) interference was also carried out, and we found that the effect of the pulse interference is better than that of single-frequency CW interference. The former is the correlation interference, and the latter is the blocking interference. Combined with the experiment phenomenon, the interference mechanism was further analyzed according to the relationship between the pulse period and the ranging code period of the navigation signal.

Alireza Dehlaghi-Ghadim, Ali Balador, Mahshid Helali Moghadam et al.

With the advent of smart industry, Industrial Control Systems (ICS) are increasingly using Cloud, IoT, and other services to meet Industry 4.0 targets. The connectivity inherent in these services exposes such systems to increased cybersecurity risks. To protect ICSs against cyberattacks, intrusion detection systems and intrusion prevention systems empowered by machine learning are used to detect abnormal behavior of the systems. Operational ICSs are not safe environments to research intrusion detection systems due to the possibility of catastrophic risks. Therefore, realistic ICS testbeds enable researchers to analyze and validate their intrusion detection algorithms in a controlled environment. Although various ICS testbeds have been developed, researchers' access to a low-cost, adaptable, and customizable testbed that can accurately simulate industrial control systems and suits security research is still an important issue. In this paper, we present ICSSIM, a framework for building customized virtual ICS security testbeds, in which various types of cyber threats and attacks can be effectively and efficiently investigated. This framework contains base classes to simulate control system components and communications. ICSSIM aims to produce extendable, versatile, reproducible, low-cost, and comprehensive ICS testbeds with realistic details and high fidelity. ICSSIM is built on top of the Docker container technology, which provides realistic network emulation and runs ICS components on isolated private operating system kernels. ICSSIM reduces the time for developing ICS components and offers physical process modelling using software and hardware in the loop simulation. We demonstrated ICSSIM by creating a testbed and validating its functionality by showing how different cyberattacks can be applied.

Rian Dolphin, Barry Smyth, Ruihai Dong

Industry classification schemes provide a taxonomy for segmenting companies based on their business activities. They are relied upon in industry and academia as an integral component of many types of financial and economic analysis. However, even modern classification schemes have failed to embrace the era of big data and remain a largely subjective undertaking prone to inconsistency and misclassification. To address this, we propose a multimodal neural model for training company embeddings, which harnesses the dynamics of both historical pricing data and financial news to learn objective company representations that capture nuanced relationships. We explain our approach in detail and highlight the utility of the embeddings through several case studies and application to the downstream task of industry classification.

Mostafa Saidur Rahim Khan, Naheed Rabbani

This study examines the growth potential of the market leader and market challenger in Japan’s telecommunications services industry. We focus on Nippon Telegraph and Telephone Corporation (NTT) and KDDI, the market leader and challenger (respectively) in terms of sales revenue, total assets, and market share. Following finance literatures, we use higher values of price–earnings ratio (P/E) and market-to-book-value-of-equity ratio (MV/BV) as the indicators of growth potential. High growth firms have the potential to outperform the overall market over a significant period of time providing a good investment opportunity for retail and institutional investors. This study uses financial data of the NTT and KDDI from the period between 2001 and 2016 and applies several regression models to examine the growth potential of the market leader and market challenger in Japan’s telecommunications services industry. Using the P/E and MV/BV as indicators of growth potential, we show that the market challenger’s growth potential is significantly higher than that of the market leader, even after controlling for firm size, liquidity, profitability, leverage, cash flow, and age.

Andrew Wuensche, José Manuel Gómez Soto

To respect physics and nature, cellular automata (CA) models of self-organisation, emergence, computation and logical universality should be isotropic, having equivalent dynamics in all directions. We present a novel paradigm, the iso-rule, a concise expression for isotropic CA by the output table for each isotropic neighborhood group, allowing an efficient method of navigating and exploring iso-rule-space. We describe new functions and tools in DDLab to generate iso-groups and iso-rules, for multi-value as well as binary, in one, two and three dimensions. These methods include filing, filtering, mutating, analysing dynamics by input-frequency and entropy, identifying the critical iso-groups for glider-gun/eater dynamics, and automatically classifying iso-rule-space. We illustrate these ideas and methods for two dimensional CA on square and hexagonal lattices.