Comparison of CMS Resistive Plate Chambers performance during LHC RUN-1 and RUN-2

The Resistive Plate Chambers detector system at the CMS experiment at the LHC provides robustness and redundancy to the muon trigger. A total of 1056 double-gap chambers cover the pseudo-rapidity region < 1.6. The main detector parameters and environmental conditions are constantly and closely monitored to achieve operational stability and high quality data in the harsh conditions of the second run period of the LHC with center-of-mass energy of 13 TeV. First results of overall detector stability with 2015 data and comparisons with data from the LHC RUN-1 period at 8 TeV are presented

DDH-MAC: a novel dynamic de-centralized hybrid MAC protocol for cognitive radio networks

The radio spectrum (3kHz - 300GHz) has become saturated and proven to be insufficient to address the proliferation of new wireless applications. Cognitive Radio Technology which is an opportunistic network and is equipped with fully programmable wireless devices that empowers the network by OODA cycle and then make intelligent decisions by adapting their MAC and physical layer characteristics such as waveform, has appeared to be the only solution for current low spectrum availability and under utilization problem. In this paper a novel Dynamic De-Centralized Hybrid “DDH-MAC” protocol for Cognitive Radio Networks has been presented which lies between Global Common Control Channel (GCCC) and non-GCCC categories of cognitive radio MAC protocols. DDH-MAC is equipped with the best features of GCCC MAC protocols but also overcomes the saturation and security issues in GCCC. To the best of authors' knowledge, DDH-MAC is the first protocol which is hybrid between GCCC and non-GCCC family of protocols. DDH-MAC provides multiple levels of security and partially use GCCC to transmit beacon which sets and announces local control channel for exchange of free channel list (FCL) sensed by the co-operatively communicating cognitive radio nodes, subsequently providing secure transactions among participating nodes over the decided local control channel. This paper describes the framework of the DDH-MAC protocol in addition to its pseudo code for implementation; it is shown that the pre-transmission time for DDH-MAC is on average 20% better while compared to other cognitive radio MAC protocols

Synthesis of Novel Nano-Strawberry TiO2 Structures with the Aid of Microwave Inverter System: Growth Time Effect on Optical Absorption Intensity

A novel anatase TiO2 with nanostrawberry-like structure with high porosity has been synthesised on ITO, with the aid of microwave power in a very short duration of 6 minutes. The growth of these novel TiO2nanostructures on ITO is attained stoichiometrically by using ammonium hexafluoro titanate, Hexamethylenetetramine, and Boric acid as precursor, capping agent, and reducing agent, respectively. Optical absorption intensity and thickness of these nanostructure layers can be varied by the growth time. A highly porous, 2.25 µm thickest layer has been successfully synthesised on ITO, and the average diameter of these nanostructures was found approximately 70±2.5nm. These highly porous nanostructures are expected to be good candidate for photocatlysis applications and efficient photovoltaic performances of dye sensitised solar cells

A novel multi-fold security framework for cognitive radio wireless ad-hoc networks

Cognitive Radio (CR) Technology has emerged as a smart and intelligent technology to address the problem of spectrum scarcity and its under-utilization. CR nodes sense the environment for vacant channels, exchange control information, and agree upon free channels list (FCL) to use for data transmission and conclusion. CR technology is heavily dependent on the control channel to dialogue on the exchanged control information which is usually in the Industrial-Scientific-Medical (ISM) band. As the ISM band is publically available this makes the CR network more prone to security vulnerabilities and flaws. In this paper a novel multi-fold security framework for cognitive radio wireless ad-hoc networks has been proposed. Multiple security levels, such as, encryption of beacon frame and privately exchanging the FCL, and the dynamic and adaptive behaviour of the framework makes the proposed protocol more resilient and secure against the traditional security attacks when compared with existing protocols

The Impact of Annotation Guidelines and Annotated Data on Extracting App Features from App Reviews

Annotation guidelines used to guide the annotation of training and evaluation datasets can have a considerable impact on the quality of machine learning models. In this study, we explore the effects of annotation guidelines on the quality of app feature extraction models. As a main result, we propose several changes to the existing annotation guidelines with a goal of making the extracted app features more useful and informative to the app developers. We test the proposed changes via simulating the application of the new annotation guidelines and then evaluating the performance of the supervised machine learning models trained on datasets annotated with initial and simulated guidelines. While the overall performance of automatic app feature extraction remains the same as compared to the model trained on the dataset with initial annotations, the features extracted by the model trained on the dataset with simulated new annotations are less noisy and more informative to the app developers. Secondly, we are interested in what kind of annotated training data is necessary for training an automatic app feature extraction model. In particular, we explore whether the training set should contain annotated app reviews from those apps/app categories on which the model is subsequently planned to be applied, or is it sufficient to have annotated app reviews from any app available for training, even when these apps are from very different categories compared to the test app. Our experiments show that having annotated training reviews from the test app is not necessary although including them into training set helps to improve recall. Furthermore, we test whether augmenting the training set with annotated product reviews helps to improve the performance of app feature extraction. We find that the models trained on augmented training set lead to improved recall but at the cost of the drop in precision