Chemical on Pleurotusostreatus

Oyster mushroom (Pleurotusostreatus) is a fungus that is much-loved by the people. In addition to deliciousness, oyster mushrooms are also very beneficial for health. High nutritional content with a variety of essential amino acids contained therein, oyster mushrooms also contain other compounds that are important for the medical aspects. The more days of oyster mushrooms increasingly interested in the community, this is apparently a result of the impact of increasing public awareness of the benefits and nutritional value of oyster mushrooms. In nature, Oyster mushroom grows only in certain seasons in limited quantities so that the oyster mushroom has good prospects to be cultivated. Mushrooms Lampung is a business that is engaged in the cultivation of oyster mushrooms. The oyster mushroom is grown on the media can be sawdust packed in plastic bags. In the oyster mushroom cultivation activities include preparation of tools and materials, raw material preparation, mixing media, composting, pasteurization, inoculation, incubation, growth and maintenance, harvesting, post-harvest, and marketing. The pasteurization process using oyster mushrooms media banker and a vessel of water

On extracting user-centric knowledge for personalised quality of service in 5G networks

©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper aims to improve the user Quality of Service (QoS) in 5G networks by introducing a user-centric view that exploits the predictability of the user’s daily motifs. An agglomerative clustering is used to identify these motifs according to the cells in which the user is camping during the day. Then, a technique to extract the personalised QoS observed by the user is proposed. The methodology is illustrated with an example that makes use of real measurements obtained from a specific customer of a 3G/4G operator. The presented results illustrate that the proposed user-centric approach is able to identify situations with poor user perceived QoS which could not be identified by a classical network-centric approach.Peer ReviewedPostprint (author's final draft

Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks

Project 023304 UIDB/04111/2020To help telecommunication operators in their network planning, namely coverage estimation and optimisation tasks, this article presents a comparison between a semi-empirical propagation model and a propagation model generated using Artificial Intelligence (AI). These two types of propagation models are quite different in their design. The semi-empiric Automatically Calibrated Standard Propagation Model (ACSPM) is specific for an operating antenna, being calibrated every time a use case application is used and the Artificial Intelligence Propagation Model (AIPM) can be applied in different scenarios, once trained, allowing to estimate coverage for a new antenna location, using information from neighboring antennas. These models have quite different features and applicability. The ACSPM should be applied in network optimisation, when using data from the current state of the antennas. The AIPM can be used in the deployment of new antennas, as it uses data from a certain geographical area. For a better comparison of the models studied, extensive Drive Tests (DT) collection campaigns conducted by operators are used, since coverage estimations are more realistic when DTs are considered. Both models are generated using very different methodologies, but their resulting performance is very similar. The AIPM achieves a Mean Absolute Error (MAE) up to 6.1 dB with a standard deviation of 4 dB. When compared to the ACSPM we have an improvement of 0.5 dB, since this only achieves a MAE up to 6.6 dB. AIPM achieves better results and is the characterised for being completely agnostic and definition-free, when compared with known propagation models.publishersversionpublishe

Synthetic Generation of Realistic Signal Strength Data to Enable 5G Rogue Base Station Investigation in Vehicular Platooning

Rogue Base Stations (RBS), also known as 5G Subscription Concealed Identifier (SUCI) catchers, were initially developed to maliciously intercept subscribers’ identities. Since then, further advances have been made, not only in RBSs, but also in communication network security. The identification and prevention of RBSs in Fifth Generation (5G) networks are among the main security challenges for users and network infrastructure. The security architecture group in 3GPP clarified that the radio configuration information received from user equipment could contain fingerprints of the RBS. This information is periodically included in the measurement report generated by the user equipment to report location information and Received Signal Strength (RSS) measurements for the strongest base stations. The motivation in this work, then is to generate 5G measurement reports to provide a large and realistic dataset of radio information and RSS measurements for an autonomous vehicle driving along various sections of a road. These simulated measurement reports can then be used to develop and test new methods for identifying an RBS and taking mitigating actions. The proposed approach can generate 20 min of synthetic drive test data in 15 s, which is 80 times faster than real time

Mobile edge computing-based data-driven deep learning framework for anomaly detection

5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the anomaly—outage and sudden hype in traffic activity that may result in congestion—detection in mobile cellular networks. This indicates wasting limited resources that ultimately leads to an elevated operational expenditure (OPEX) and also interrupting quality of service (QoS) and quality of experience (QoE). Motivated by the outstanding success of deep learning (DL) technology, our study applies it for detection of the above-mentioned anomalies and also supports mobile edge computing (MEC) paradigm in which core network (CN)’s computations are divided across the cellular infrastructure among different MEC servers (co-located with base stations), to relief the CN. Each server monitors user activities of multiple cells and utilizes $L$ -layer feedforward deep neural network (DNN) fueled by real call detail record (CDR) dataset for anomaly detection. Our framework achieved 98.8% accuracy with 0.44% false positive rate (FPR)—notable improvements that surmount the deficiencies of the old studies. The numerical results explicate the usefulness and dominance of our proposed detector

Forecast scheduling for mobile users

International audienceIn future networks, Radio Resource Management (RRM) could benefit from Geo-Localized Measurements (GLM) thanks to the Minimization of Drive Testing (MDT) feature introduced in Long Term Evolution (LTE). Such measurements can be processed by the network and be used to optimize its performance. The purpose of this paper a is to use GLM to significantly improve scheduling. We introduce the concept of forecast scheduler for users in high mobility that exploit GLM. It is assumed that a Radio Environment Map (REM) can provide interpolated Signal to Interference plus Noise Ratio (SINR) values along the user trajectories. The diversity in the mean SINR values of the users during a time interval of several seconds allows to achieve a significant performance gain. The forecast scheduling is formulated as a convex optimization problem namely the maximization of an α−fair utility function of the cumulated rates of the users along their trajectories. Numerical results for thee different mobility scenarios illustrate the important performance gain achievable by the forecast scheduler. Index Terms—Forecast scheduler, alfa-fair, high mobility, Minimizing Drive Tests, MDT, Radio Environment Maps, REM, geo-localized measurement

On the Improvement of Cellular Coverage Maps by Filtering MDT Measurements

In cellular systems, network re-planning aims to update network configuration to cope with permanent changes in the environment. In this task, terminal measurements are often used to calibrate performance models integrated in radio network planning tools. In Release 10 of the 3GPP standard, the Minimization of Drive Test (MDT) feature allows the collection of user position correlated to performance statistics or radio events. In practice, positioning errors severely limit the potential of MDT measurements. In this work, a preliminary analysis of a large MDT dataset taken from a commercial Long-Term Evolution (LTE) network shows for the first time several sources of positioning errors not previously reported in the literature. Then, a heuristic filtering algorithm is proposed to discard samples with inaccurate location data. Method assessment is done by checking the impact of filtering on the coverage map built with a real MDT dataset. Results show that the proposed method significantly improves the accuracy of coverage maps by filtering unreliable measurements.European Union’s Horizon 2020 Research and Innovation Programme under the Project H2020 LOCUS under (Grant 871249