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

Alfred D. Chandler

Alpana Dubey, Suma Mani Kuriakose, Nitish Bhardwaj

We propose an approach to generate synthetic data to train computer vision (CV) models for industrial wear and tear detection. Wear and tear detection is an important CV problem for predictive maintenance tasks in any industry. However, data curation for training such models is expensive and time-consuming due to the unavailability of datasets for different wear and tear scenarios. Our approach employs a vision language model along with a 3D simulation and rendering engine to generate synthetic data for varying rust conditions. We evaluate our approach by training a CV model for rust detection using the generated dataset and tested the trained model on real images of rusted industrial objects. The model trained with the synthetic data generated by our approach, outperforms the other approaches with a mAP50 score of 0.87. The approach is customizable and can be easily extended to other industrial wear and tear detection scenarios

Jingze Ding, Zijian Zhou, Bingli Jiao et al.

Polarforming is a promising technique that enables dynamic adjustment of antenna polarization to mitigate depolarization effects commonly encountered during electromagnetic (EM) wave propagation. In this letter, we investigate the polarforming design for secure wireless communication systems, where the base station (BS) is equipped with polarization-reconfigurable antennas (PRAs) and can flexibly adjust the antenna polarization to transmit confidential information to a legitimate user in the presence of an eavesdropper. To maximize the achievable secrecy rate, we propose an efficient iterative algorithm to jointly optimize transmit beamforming and polarforming, where beamforming exploits spatial degrees of freedom (DoFs) to steer the transmit beam toward the user, while polarforming leverages polarization DoFs to align the polarization state of the EM wave received by the user with that of its antenna. Simulation results demonstrate that, compared to conventional fixed-polarization antenna (FPA) systems, polarforming can fully exploit the DoFs in antenna polarization optimization to significantly enhance the security performance of wireless communication systems.

Muhammad Ahmed Mohsin, Sagnik Bhattacharya, Abhiram Gorle et al.

Wireless support of virtual reality (VR) has challenges when a network has multiple users, particularly for 3D VR gaming, digital AI avatars, and remote team collaboration. This work addresses these challenges through investigation of the low-rank channels that inevitably occur when there are more active users than there are degrees of spatial freedom, effectively often the number of antennas. The presented approach uses optimal nonlinear transceivers, equivalently generalized decision-feedback or successive cancellation for uplink and superposition or dirty-paper precoders for downlink. Additionally, a powerful optimization approach for the users' energy allocation and decoding order appears to provide large improvements over existing methods, effectively nearing theoretical optima. As the latter optimization methods pose real-time challenges, approximations using deep reinforcement learning (DRL) are used to approximate best performance with much lower (5x at least) complexity. Experimental results show significantly larger sum rates and very large power savings to attain the data rates found necessary to support VR. Experimental results show the proposed algorithm outperforms current industry standards like orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), as well as the highly researched methods in multi-carrier NOMA (MC-NOMA), enhancing sum data rate by 39%, 28%, and 16%, respectively, at a given power level. For the same data rate, it achieves power savings of 75%, 45%, and 40%, making it ideal for VR applications. Additionally, a near-optimal deep reinforcement learning (DRL)-based resource allocation framework for real-time use by being 5x faster and reaching 83% of the global optimum is introduced.

Hai Dinh-Tuan

The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 exposes the limitations of traditional, hierarchical architectures like ISA-95 and RAMI 4.0. Their inherent rigidity, data silos, and lack of support for cloud-native technologies impair the development of scalable and interoperable industrial systems. This paper addresses this issue by introducing CRACI, a Cloud-native Reference Architecture for the Industrial Compute Continuum. Among other features, CRACI promotes a decoupled and event-driven model to enable flexible, non-hierarchical data flows across the continuum. It embeds cross-cutting concerns as foundational pillars: Trust, Governance & Policy, Observability, and Lifecycle Management, ensuring quality attributes are core to the design. The proposed architecture is validated through a two-fold approach: (1) a comparative theoretical analysis against established standards, operational models, and academic proposals; and (2) a quantitative evaluation based on performance data from previously published real-world smart manufacturing implementations. The results demonstrate that CRACI provides a viable, state-of-the-art architecture that utilizes the compute continuum to overcome the structural limitations of legacy models and enable scalable, modern industrial systems.

Victor U. Ekpu, Abraham Brown

Background Tobacco smoking is the cause of many preventable diseases and premature deaths in the UK and around the world. It poses enormous health- and non-health-related costs to the affected individuals, employers, and the society at large. The World Health Organization (WHO) estimates that, globally, smoking causes over US$500 billion in economic damage each year. Objectives This paper examines global and UK evidence on the economic impact of smoking prevalence and evaluates the effectiveness and cost effectiveness of smoking cessation measures. Study Selection Search Methods We used two major health care/economic research databases, namely PubMed and the National Institute for Health Research (NIHR) database that contains the British National Health Service (NHS) Economic Evaluation Database; Cochrane Library of systematic reviews in health care and health policy; and other health-care-related bibliographic sources. We also performed hand searching of relevant articles, health reports, and white papers issued by government bodies, international health organizations, and health intervention campaign agencies. Selection Criteria The paper includes cost-effectiveness studies from medical journals, health reports, and white papers published between 1992 and July 2014, but included only eight relevant studies before 1992. Most of the papers reviewed reported outcomes on smoking prevalence, as well as the direct and indirect costs of smoking and the costs and benefits of smoking cessation interventions. We excluded papers that merely described the effectiveness of an intervention without including economic or cost considerations. We also excluded papers that combine smoking cessation with the reduction in the risk of other diseases. Data Collection and Analysis The included studies were assessed against criteria indicated in the Cochrane Reviewers Handbook version 5.0.0. Outcomes Assessed in the Review Primary outcomes of the selected studies are smoking prevalence, direct and indirect costs of smoking, and the costs and benefits of smoking cessation interventions (eg, “cost per quitter”, “cost per life year saved”, “cost per quality-adjusted life year gained,” “present value” or “net benefits” from smoking cessation, and “cost savings” from personal health care expenditure). Main Results The main findings of this study are as follows: 1. The costs of smoking can be classified into direct, indirect, and intangible costs. About 15% of the aggregate health care expenditure in high-income countries can be attributed to smoking. In the US, the proportion of health care expenditure attributable to smoking ranges between 6% and 18% across different states. In the UK, the direct costs of smoking to the NHS have been estimated at between £2.7 billion and £5.2 billion, which is equivalent to around 5% of the total NHS budget each year. The economic burden of smoking estimated in terms of GDP reveals that smoking accounts for approximately 0.7% of China's GDP and approximately 1% of US GDP. As part of the indirect (non-health-related) costs of smoking, the total productivity losses caused by smoking each year in the US have been estimated at US$151 billion. 2. The costs of smoking notwithstanding, it produces some potential economic benefits. The economic activities generated from the production and consumption of tobacco provides economic stimulus. It also produces huge tax revenues for most governments, especially in high-income countries, as well as employment in the tobacco industry. Income from the tobacco industry accounts for up to 7.4% of centrally collected government revenue in China. Smoking also yields cost savings in pension payments from the premature death of smokers. 3. Smoking cessation measures could range from pharmacological treatment interventions to policy-based measures, community-based interventions, telecoms, media, and technology (TMT)-based interventions, school-based interventions, and workplace interventions. 4. The cost per life year saved from the use of pharmacological treatment interventions ranged between US$128 and US$1,450 and up to US$4,400 per quality-adjusted life years (QALYs) saved. The use of pharmacotherapies such as varenicline, NRT, and Bupropion, when combined with GP counseling or other behavioral treatment interventions (such as proactive telephone counseling and Web-based delivery), is both clinically effective and cost effective to primary health care providers. 5. Price-based policy measures such as increase in tobacco taxes are unarguably the most effective means of reducing the consumption of tobacco. A 10% tax-induced cigarette price increase anywhere in the world reduces smoking prevalence by between 4% and 8%. Net public benefits from tobacco tax, however, remain positive only when tax rates are between 42.9% and 91.1%. The cost effectiveness ratio of implementing non-price-based smoking cessation legislations (such as smoking restrictions in work places, public places, bans on tobacco advertisement, and raising the legal age of smokers) range from US$2 to US$112 per life year gained (LYG) while reducing smoking prevalence by up to 30%–82% in the long term (over a 50-year period). 6. Smoking cessation classes are known to be most effective among community-based measures, as they could lead to a quit rate of up to 35%, but they usually incur higher costs than other measures such as self-help quit-smoking kits. On average, community pharmacist-based smoking cessation programs yield cost savings to the health system of between US$500 and US$614 per LYG. 7. Advertising media, telecommunications, and other technology-based interventions (such as TV, radio, print, telephone, the Internet, PC, and other electronic media) usually have positive synergistic effects in reducing smoking prevalence especially when combined to deliver smoking cessation messages and counseling support. However, the outcomes on the cost effectiveness of TMT-based measures have been inconsistent, and this made it difficult to attribute results to specific media. The differences in reported cost effectiveness may be partly attributed to varying methodological approaches including varying parametric inputs, differences in national contexts, differences in advertising campaigns tested on different media, and disparate levels of resourcing between campaigns. Due to its universal reach and low implementation costs, online campaign appears to be substantially more cost effective than other media, though it may not be as effective in reducing smoking prevalence. 8. School-based smoking prevalence programs tend to reduce short-term smoking prevalence by between 30% and 70%. Total intervention costs could range from US$16,400 to US$580,000 depending on the scale and scope of intervention. The cost effectiveness of school-based programs show that one could expect a saving of approximately between US$2,000 and US$20,000 per QALY saved due to averted smoking after 2–4 years of follow-up. 9. Workplace-based interventions could represent a sound economic investment to both employers and the society at large, achieving a benefit–cost ratio of up to 8.75 and generating 12-month employer cost savings of between $150 and $540 per nonsmoking employee. Implementing smoke-free workplaces would also produce myriads of new quitters and reduce the amount of cigarette consumption, leading to cost savings in direct medical costs to primary health care providers. Workplace interventions are, however, likely to yield far greater economic benefits over the long term, as reduced prevalence will lead to a healthier and more productive workforce. Conclusions We conclude that the direct costs and externalities to society of smoking far outweigh any benefits that might be accruable at least when considered from the perspective of socially desirable outcomes (ie, in terms of a healthy population and a productive workforce). There are enormous differences in the application and economic measurement of smoking cessation measures across various types of interventions, methodologies, countries, economic settings, and health care systems, and these may have affected the comparability of the results of the studies reviewed. However, on the balance of probabilities, most of the cessation measures reviewed have not only proved effective but also cost effective in delivering the much desired cost savings and net gains to individuals and primary health care providers.

Mohammed Q. Shormani, Yehia A. Al-Sohbani

This study provides a comprehensive analysis of artificial intelligence (AI) contribution to research in the translation industry (ACTI), synthesizing it over forty-five years from 1980-2024. 13220 articles were retrieved from three sources, namely WoS, Scopus, and Lens; 9836 were unique records, which were used for the analysis. We provided two types of analysis, viz., scientometric and thematic, focusing on Cluster, Subject categories, Keywords, Bursts, Centrality and Research Centers as for the former. For the latter, we provided a thematic review for 18 articles, selected purposefully from the articles involved, centering on purpose, approach, findings, and contribution to ACTI future directions. This study is significant for its valuable contribution to ACTI knowledge production over 45 years, emphasizing several trending issues and hotspots including Machine translation, Statistical machine translation, Low-resource language, Large language model, Arabic dialects, Translation quality, and Neural machine translation. The findings reveal that the more AI develops, the more it contributes to translation industry, as Neural Networking Algorithms have been incorporated and Deep Language Learning Models like ChatGPT have been launched. However, much rigorous research is still needed to overcome several problems encountering translation industry, specifically concerning low-resource, multi-dialectical and free word order languages, and cultural and religious registers.

Qunhang Fu, Fei Wang, Mengdie Zhu et al.

Wireless sensing has made significant progress in tasks ranging from action recognition, vital sign estimation, pose estimation, etc. After over a decade of work, wireless sensing currently stands at the tipping point transitioning from proof-of-concept systems to the large-scale deployment. We envision a future service scenario where wireless sensing service providers distribute sensing models to users. During usage, users might request new sensing capabilities. For example, if someone is away from home on a business trip or vacation for an extended period, they may want a new sensing capability that can detect falls in elderly parents or grandparents and promptly alert them. In this paper, we propose CCS (continuous customized service), enabling model updates on users' local computing resources without data transmission to the service providers. To address the issue of catastrophic forgetting in model updates where updating model parameters to implement new capabilities leads to the loss of existing capabilities we design knowledge distillation and weight alignment modules. These modules enable the sensing model to acquire new capabilities while retaining the existing ones. We conducted extensive experiments on the large-scale XRF55 dataset across Wi-Fi, millimeter-wave radar, and RFID modalities to simulate scenarios where four users sequentially introduced new customized demands. The results affirm that CCS excels in continuous model services across all the above wireless modalities, significantly outperforming existing approaches like OneFi.

Bingwen Chen, Xintong Ling, Weihang Cao et al.

The coexistence of heterogeneous sub-networks in 6G poses new security and trust concerns and thus calls for a perimeterless-security model. Blockchain radio access network (B-RAN) provides a trust-building approach via repeated interactions rather than relying on pre-established trust or central authentication. Such a trust-building process naturally supports dynamic trusted services across various service providers (SP) without the need for perimeter-based authentications; however, it remains vulnerable to environmental and system unreliability such as wireless channel uncertainty. In this study, we investigate channel unreliability in the trust-building framework based on repeated interactions for secure wireless services. We derive specific requirements for achieving cooperation between SPs and clients via a repeated game model and illustrate the implications of channel unreliability on sustaining trusted wireless services. We consider the framework design and optimization to guarantee SP-client cooperation, given the worst channel condition and/or the least cooperation willingness. Furthermore, we explore the maximum cooperation area to enhance service resilience and reveal the trade-off relationship between transmission efficiency, security integrity, and cooperative margin. Finally, we present simulations to demonstrate the system performance over fading channels and verify our results.

Tracy Patricia Nindry Abigail Rolnmuch, Yuhana Astuti

The realization of FDI and DDI from January to December 2022 reached Rp1,207.2 trillion. The largest FDI investment realization by sector was led by the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industry sector, followed by the Mining sector and the Electricity, Gas, and Water sector. The uneven amount of FDI investment realization in each industry and the impact of the COVID-19 pandemic in Indonesia are the main issues addressed in this study. This study aims to identify the factors that influence the entry of FDI into industries in Indonesia and measure the extent of these factors' influence on the entry of FDI. In this study, classical assumption tests and hypothesis tests are conducted to investigate whether the research model is robust enough to provide strategic options nationally. Moreover, this study uses the ordinary least squares (OLS) method. The results show that the electricity factor does not influence FDI inflows in the three industries. The Human Development Index (HDI) factor has a significant negative effect on FDI in the Mining Industry and a significant positive effect on FDI in the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industries. However, HDI does not influence FDI in the Electricity, Gas, and Water Industries in Indonesia.

Helen Atherton, Abi Eccles, Leon Poltawski et al.

Background Online appointment booking is a commonly used tool in several industries. There is limited evidence about the benefits and challenges of using online appointment booking in health care settings. Potential benefits include convenience and the ability to track appointments, although some groups of patients may find it harder to engage with online appointment booking. We sought to understand how patients in England used and experienced online appointment booking. Objective This study aims to describe and compare the characteristics of patients in relation to their use of online appointment booking in general practice and investigate patients’ views regarding online appointment booking arrangements. Methods This was a mixed methods study set in English general practice comprising a retrospective analysis of the General Practice Patient Survey (GPPS) and semistructured interviews with patients. Data used in the retrospective analysis comprised responses to the 2018 and 2019 GPPS analyzed using mixed-effects logistic regression. Semistructured interviews with purposively sampled patients from 11 general practices in England explored experiences of and views on online appointment booking. Framework analysis was used to allow for comparison with the findings of the retrospective analysis. Results The retrospective analysis included 1,327,693 GPPS responders (2018-2019 combined). We conducted 43 interviews with patients with a variety of experiences and awareness of online appointment booking; of these 43 patients, 6 (14%) were from ethnic minority groups. In the retrospective analysis, more patients were aware that online appointment booking was available (581,224/1,288,341, 45.11%) than had experience using it (203,184/1,301,694, 15.61%). There were deprivation gradients for awareness and use and a substantial decline in both awareness and use in patients aged >75 years. For interview participants, age and life stage were factors influencing experiences and perceptions, working patients valued convenience, and older patients preferred to use the telephone. Patients with long-term conditions were more aware of (odds ratio [OR] 1.43, 95% CI 1.41-1.44) and more likely to use (OR 1.65, 95% CI 1.63-1.67) online appointment booking. Interview participants with long-term conditions described online appointment booking as useful for routine nonurgent appointments. Patients in deprived areas were clustered in practices with low awareness and use of online appointment booking among GPPS respondents (OR for use 0.65, 95% CI 0.64-0.67). Other key findings included the influence of the availability of appointments online and differences in the registration process for accessing online booking. Conclusions Whether and how patients engage with online appointment booking is influenced by the practice with which they are registered, whether they live with long-term conditions, and their deprivation status. These factors should be considered in designing and implementing online appointment booking and have implications for patient engagement with the wider range of online services offered in general practice.

Yong Xia, Xiaowei Shi

In this paper, a new viewpoint on the function of the Schottky barrier diode (SBD) in microwave rectifying circuit is proposed. In the analog circuit field, it has been formed a relatively mature theoretical system about the rectifying circuit, which constitutes the base of subsequent rectifier design schemes. The researchers take it for granted that the SBD selected as the core component in the microwave rectifying circuit should play the role of rectifying in the same way as the diode in the analog circuit. The fact that the one-directional conductivity of the SBD is not observed in microwave circuit by the means of simulation and experiment is proposed. The basis of the former rectification theory is in doubt here. Due to the interaction of the microstrip line and SBD, the DC component generated at the front end of SBD is the key reason for the DC output power of rectifying circuit. The way of DC generation is completely different from the previous idea. Accurate and in-depth understanding of the function of SBD in microwave rectifying circuit will help the researchers to make use of SBD rightly and design circuit in future applications.

Tobias Kallehauge, Martin Voigt Vejling, Pablo Ramìrez-Espinosa et al.

Location information is often used as a proxy to guarantee the performance of a wireless communication link. However, localization errors can result in a significant mismatch with the guarantees, particularly detrimental to users operating the ultra-reliable low-latency communication (URLLC) regime. This paper unveils the fundamental statistical relations between location estimation uncertainty and wireless link reliability, specifically in the context of rate selection for ultra-reliable communication. We start with a simple one-dimensional narrowband Rayleigh fading scenario and build towards a two-dimensional scenario in a rich scattering environment. The wireless link reliability is characterized by the meta-probability, the probability with respect to localization error of exceeding the outage capacity, and by removing other sources of errors in the system, we show that reliability is sensitive to localization errors. The $ε$-outage coherence radius is defined and shown to provide valuable insight into the problem of location-based rate selection. However, it is generally challenging to guarantee reliability without accurate knowledge of the propagation environment. Finally, several rate-selection schemes are proposed, showcasing the problem's dynamics and revealing that properly accounting for the localization error is critical to ensure good performance in terms of reliability and achievable throughput.

Inoj Neupane, Belal Alsinglawi, Khaled Rabie

Humans and robots working together in an environment to enhance human performance is the aim of Industry 5.0. Although significant progress in outdoor positioning has been seen, indoor positioning remains a challenge. In this paper, we introduce a new research concept by exploiting the potential of indoor positioning for Industry 5.0. We use Wi-Fi Received Signal Strength Indicator (RSSI) with trilateration using cheap and easily available ESP32 Arduino boards for positioning as well as sending effective route signals to a human and a robot working in a simulated-indoor factory environment in real-time. We utilized machine learning models to detect safe closeness between two co-workers (a human subject and a robot). Experimental data and analysis show an average deviation of less than 1m from the actual distance while the targets are mobile or stationary.

Amani Abusafia, Athman Bouguettaya, Abdallah Lakhdari

We propose a novel Quality of Experience (QoE) metric as a key criterion to optimize the composition of energy services in a crowdsourced IoT environment. A novel importance-based composition algorithm is proposed to ensure the highest QoE for consumers. A set of experiments is conducted to evaluate the proposed approaches' effectiveness and efficiency.

Tim Lackorzynski, Sebastian Rehms, Tao Li et al.

Trends like Industry 4.0 will pose new challenges for future industrial networks. Greater interconnectedness, higher data volumes as well as new requirements for speeds as well as security will make new approaches necessary. Performanceoptimized networking techniques will be demanded to implement new use cases, like network separation and isolation, in a secure fashion. A new and highly efficient protocol, that will be vital for that purpose, is MACsec. It is a Layer 2 encryption protocol that was previously extended specifically for industrial environments. Yet, it lacks the ability to bridge local networks. Therefore, in this work, we propose a secure and efficient Layer 3 tunneling scheme for MACsec. We design and implement two approaches, that are equally secure and considerably outperform comparable state-of-the-art techniques.

Apoorva Jaiswal, Abhishek Mitra

Assigning appropriate industry tag(s) to a company is a critical task in a financial institution as it impacts various financial machineries. Yet, it remains a complex task. Typically, such industry tags are to be assigned by Subject Matter Experts (SME) after evaluating company business lines against the industry definitions. It becomes even more challenging as companies continue to add new businesses and newer industry definitions are formed. Given the periodicity of the task it is reasonable to assume that an Artificial Intelligent (AI) agent could be developed to carry it out in an efficient manner. While this is an exciting prospect, the challenges appear from the need of historical patterns of such tag assignments (or Labeling). Labeling is often considered the most expensive task in Machine Learning (ML) due its dependency on SMEs and manual efforts. Therefore, often, in enterprise set up, an ML project encounters noisy and dependent labels. Such labels create technical hindrances for ML Models to produce robust tag assignments. We propose an ML pipeline which uses semantic similarity matching as an alternative to multi label text classification, while making use of a Label Similarity Matrix and a minimum labeling strategy. We demonstrate this pipeline achieves significant improvements over the noise and exhibit robust predictive capabilities.

Michael Gundall, Calvin Glas, Hans D. Schotten

Industry 4.0 offers many possibilities for creating highly efficient and flexible manufacturing. To create such advantages, highly automated and thus digitized processes and systems are required. Here, most technologies known from the office floor are basically suitable for these tasks, but cannot meet the high demands of industrial use cases. Therefore, they cannot replace industrial technologies and devices that have performed well over decades "out of the box". For this reason, many technologies known from the office floor are being investigated and adapted for industrial environments. An important task is the virtualization of process controls, as more and more devices use computation offloading, e.g. due to limited resources. In this paper we extend the work on our novel architecture that enables numerous use cases and meets industrial requirements by virtualizing process controllers. In addition, a testbed based on a factory scenario is proposed to evaluate the most important features of the presented architecture.

Qinghua Liu, Kai Ding, Bingsen Wu et al.

For the traditional target localization algorithms of frequency diverse array (FDA), there are some problems such as angle and distance coupling in single-frequency receiving FDA mode, large amount of calculation, and weak adaptability. This paper introduces a good learning and predictive method of target localization by using BP neural network on FDA, and FDA-IPSO-BP neural network algorithm is formed. The improved particle swarm optimization (IPSO) algorithm with nonlinear weights is developed to optimize the neural network weights and biases to prevent BP neural network from easily falling into local minimum points. In addition, the decoupling of angle and distance with single frequency increment is well solved. The simulation experiments show that the proposed algorithm has better target localization effect and convergence speed, compared with FDA-BP and FDA-MUSIC algorithms.

Zhang Lin, Jiang Yicheng

In this study, a phased array radar was used to accurately image stationary and moving ship targets on the vast sea surface. To solve the challenge in real-time processing of the massive amount of data generated by phased array synthetic-aperture radar imaging, this study leveraged the block sparse characteristics of ships on the sea surface and adopted the joint block orthogonal matching pursuit algorithm to obtain high-resolution one-dimensional range images. By only estimating the azimuth Doppler parameters of the targets within the range gates, the amount of process data was significantly reduced, and the data processing speed was enhanced. The synchrosqueezing transform-STFT algorithm was introduced to perform transient imaging as a solution to the blurred imaging of ships due to the three-dimensional swing under the action of waves. The images of the targets were obtained from different squint angles of the antenna array, which improved the imaging accuracy of ships on a vast sea surface. Compared with traditional imaging algorithms, this algorithm can effectively overcome the interference of sea clutter on ship imaging and the influence of sea waves on ship wobble; it can also obtain high-resolution imaging for both stationary and moving targets in a limited amount of time.