Influence of the magnetron power on the Er-related photoluminescence of AlN:Er films prepared by magnetron sputtering

International audienceThe effect of magnetron power on the room temperature 1.54 µm infra-red photoluminescence intensity of erbium doped AlN films grown by r. f. magnetron sputtering, has been studied. The AlN:Er thin films were deposited on (001) Silicon substrates. The study presents relative photoluminescence intensities of nanocrystallized samples prepared with identical sputtering parameters for two erbium doping levels (0.5 and 1.5 atomic %). The structural evolution of the crystallites as a function of the power is followed by transmission electron microscopy. Copyright line will be provided by the publisher 1 Introduction For some time now, rare-earth (RE)-doped semiconductors represent significant potential applications in the field of opto-electronic technology. Part of this technological interest relies on the shielded 4f levels of the RE ions as they give rise to sharp and strong luminescence peaks [1-5]. Among the RE elements, Er is preferred to its counterparts since the Er ions can produce both visible light at 558 nm (green, one of the primary colours) and IR light at 1.54 µm whose spectrum region coincides with the main low-loss region in the absorption spectrum of silica-based optical fibres, combining so potential applications towards photonic devices and towards optical communication devices operating in the infrared domain. These interesting emissions can however only be exploited when placed into host matrixes. On one side, the shielding of the intra 4f levels prevents the shifting of the RE 3+ energy levels and ensures the frequency emission stability. Moreover the intra 4f transitions are parity forbidden for the isolated ions. Matrixes can render the Er 3+ ions optically active, via a relaxation of selection rules due to crystal field effects. As silicon based materials were tested in the 1960s to the 90s with no clear industrial success it was found that th

Effects of Subcapsularis Neuro Muscular Reduction (NMR) in Adhesive Capsulitis

Background: To determine the effects of Subscapularis Neuromuscular Reduction (NMR) in Adhesive Capsulitis patients on pain, Range of Motion (ROM) and Quality of life. Methods: In this randomized controlled trail patients with freezing and frozen stage of Adhesive capsulitis and limited range of movement were included. Patients were randomly divided into control (Group A) and experimental group (Group B). The patients of Group A were treated with conventional physical therapy treatment protocol and patients of group B were treated with subscapularis neuromuscular reduction along with conventional physical therapy. The patient outcome measures were assessed using numeric pain rating scale (NPRS), SPADI (shoulder pain and disability index) and ranges via goniometry. Data was analyzed by SPSS 21. Results: Both group showed significant improvement, but the end value comparison showed significant difference. NMR (Neuromuscular Reduction) on Subscapularis muscles improved the pain, ROM and Patient functional status more as compared to the conventional physical therapy group. The NPRS mean value for control group was 2.90±1.09 and mean value for experimental group was 2.05±1.10with p value of 0.021 while the mean value of SPADI for control group was33.52±9.96 and for experimental group was 26.72±8.00 with p value of 0.026. Conclusion: Treatment groups showed improvement by reducing pain, improving range of motion and functional status but neuromuscular reduction of subscapularis muscles was found to be more effective

A Hybrid Soft Computing Framework for Electrical Energy Optimization

Electricity is a significant and essential player in the modern world economy. It translates into the social, economic, and sectorial growth of any region. The scarcity of these resources demands a highly efficient and robust energy management system (EMS). In the recent literature, many artificial intelligence algorithms have been proposed to cater to the need for efficient and real-time decision-making. Moreover, the hybridization of these algorithms has also been proposed for optimum decision-making. In this paper, a hybrid soft-computing-based framework has been proposed for intelligent energy management and optimization. The proposed model has based on the evolutionary neuro-fuzzy approach that can predict the energy demand as an objective function and optimize the energy within the given constraints. The future extension of this work will be the implementation and validation of the proposed framework on either a real application dataset or dataset opted from the benchmark repositor

An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms

Data-driven electrical energy efficiency management is the emerging trend in electrical energy forecasting and management. This fusion of data science, artificial intelligence, and electrical energy management has turned out to be the most precise and robust energy management solution. The Smart Energy Informatics Lab (SEIL) of the Indian Institute of Technology (IIT) conducted an experimental study in 2019 to collect massive data on university campus energy consumption. The comprehensive comparative study preparatory to the recommendation of the best candidate out of 24 machine learning algorithms on the SEIL dataset is presented in this work. In this research work, an exhaustive parametric and empirical comparative study is conducted on the SEIL dataset for the recommendation of the optimal machine learning algorithm. The simulation results established the findings that Bagged Trees, Fine Trees, and Medium Trees are, respectively, the best-, second-best-, and third-best-performing algorithms in terms of efficacy. On the contrary, a reverse ranking is observed in terms of efficiency. This is grounded in the fact that Bagged Trees is most effective algorithm for the said application and Medium Trees is the most efficient one. Likewise, Fine Trees has the optimum tradeoff between efficacy and efficiency

A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey

The Energy Management System (EMS) is the cost-effectiveness, robustness, and flexible approach for energy efficiency management (EEM). Data-Driven Energy Efficiency Management (D2EEM) is a recent advancement in EMS. The D2EEM is the blend of data science and artificial intelligence for EEM. Due to the highly tolerant to the performance plateau and unconstraint to the feature extraction, Deep Learning (DL) facilitates handling big data-driven problems of EEM. To the best of the knowledge, the accurate and robust D2EEM is the pressing need. Moreover, the accurate pre-trained DL network for EEM is not available in the recent literature. In this work, a comprehensive study is presented to devise a D2EEM. Moreover, the architecture is suggested in connection to the research gap

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity

Towards the Digital Twin (DT) of narrow-band Internet of Things (NBIoT) wireless communication in industrial indoor environment

A study of the behavior of NB-IoT wireless communication in an industrial indoor environment was conducted in this paper. With Wireless Insite software, a scenario in the industrial sector was simulated and modeled. Our research examined how this scenario or environment affected the communication parameters of NB-IoT’s physical layer. In this context, throughput levels among terminals as well as between terminals and transceiver towers, the power received at signal destination points, signal-to-noise ratios (SNRs) in the environment, and distances between terminals and transceivers are considered. These simulated results are also compared with the calculated or theoretical values of these parameters. The results show the effect of the industrial setting on wireless communication. The differences between the theoretical and simulated values are also established