A Bibliometric Perspective Survey of IoT controlled AI based Swarm robots

Robotics is the ­new-age domain of technology that deals with bringing a collaboration of all disciplines of sciences and engineering to create a mechanical machine that may or may not work entirely independently but definitely focuses on making human lives much easier. It has repeatedly shown its ability to change lives at home and in the industry. As the field of robotics research grows and reaches new worlds, the military is one area where advances can have a significant impact, and the government is aware of this. Military technology has come a long way from the days where soldiers had to walk into traps, putting their own lives in danger for their fellow soldiers, to today, when soldiers have robots walk into the same traps with possibility and result of zero human casualties. High-risk military operations such as mine detection, bomb defusing, fighter pilot aviation, and entering enemy territory without complete knowledge of what is to come are all tasks that can be programmed in a way that makes them accustomed to scenarios like these, either by intensive machine learning algorithms or artificially intelligent robot systems. Military soldiers are human capital; they are not self-driving robots; they are living beings with emotions, fears, and weaknesses, and they will almost always be unreliable as compared to computers and robots. They are easily affected by environmental effects and are vulnerable to external influences. The government\u27s costs for deployed troops, such as training and salaries, are extremely high. As a result, the solution is to build AI robots for defence operations that can sense, collect data by observing surroundings as any human soldier would, and report it back to a workstation where it can be used for strategy building and planning on what the next step should be during a mission, thus making the army better prepared for any kind of trouble that might be on their way. In this paper, the survey and bibliometric analysis of AI-based IoT managed Swarm Robots from the Scopus repository is discussed, which analyses research by area, notable authors, organizations, funding agencies and countries. Statistical analysis of literature published as journals, articles and papers that aids in understanding the global influence of publication is called Bibliometric analysis. This paper is a thorough analysis of 84 research papers as obtained from the Scopus repository on the 3rd of April 2021. GPS Visualizer, Gephi, wordcloud, and ScienceScape are open source softwares used in the visualization review. As previously mentioned, the visualization assists in a quick and easy interpretation of the different viewpoints in a particular study domain pursuit

Performance of algorithms that reconstruct missing transverse momentum in √s= 8 TeV proton-proton collisions in the ATLAS detector

The reconstruction and calibration algorithms used to calculate missing transverse momentum (EmissT ) with the ATLAS detector exploit energy deposits in the calorimeter and tracks reconstructed in the inner detector as well as the muon spectrometer. Various strategies are used to suppress effects arising from additional proton–proton interactions, called pileup, concurrent with the hard-scatter processes. Tracking information is used to distinguish contributions from the pileup interactions using their vertex separation along the beam axis. The performance of the EmissT reconstruction algorithms, especially with respect to the amount of pileup, is evaluated using data collected in proton–proton collisions at a centre-of-mass energy of 8 TeV during 2012, and results are shown for a data sample corresponding to an integrated luminosity of 20.3fb−1. The simulation and modelling of EmissT in events containing a Z boson decaying to two charged leptons (electrons or muons) or a W boson decaying to a charged lepton and a neutrino are compared to data. The acceptance for different event topologies, with and without high transverse momentum neutrinos, is shown for a range of threshold criteria for EmissT , and estimates of the systematic uncertainties in the EmissT measurements are presented.ATLAS Collaboration, for complete list of authors see dx.doi.org/10.1140/epjc/s10052-017-4780-2Funding: We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently.We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, UK; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [58].</p