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Logarithmic singularities and quantum oscillations in magnetically doped topological insulators
Nandi, D.,Sodemann, Inti,Shain, K.,Lee, G. H.,Huang, K.-F.,Chang, Cui-Zu,Ou, Yunbo,Lee, S. P.,Ward, J.,Moodera, J. S.,Kim, P.,Yacoby, A. American Physical Society 2018 Physical Review B Vol.97 No.8
<P>We report magnetotransport measurements on magnetically doped (Bi, Sb)(2)Te-3 films grown by molecular beam epitaxy. In Hall bar devices, we observe logarithmic dependence of transport coefficients in temperature and bias voltage which can be understood to arise from electron-electron interaction corrections to the conductivity and self-heating. Submicron scale devices exhibit intriguing quantum oscillations at high magnetic fields with dependence on bias voltage. The observed quantum oscillations can be attributed to bulk and surface transport.</P>
Reddy, K.,Rao, S.,Inti, R.,Young, B.,Elshazly, A.,Talegaonkar, M.,Hanumolu, P. K. IEEE 2012 IEEE journal of solid-state circuits Vol.47 No.12
<P>This paper presents a continuous-time (CT) ΔΣ modulator using a VCO-based internal quantizer. It incorporates a nonlinear VCO as the second stage in a two-stage residue canceling quantizer (RCQ) and mitigates the impact of its nonlinearity by spanning only a small region of the VCO's V-to-F nonlinear tuning curve. The order of noise shaping is increased by placing the RCQ in a continuous-time ΔΣ loop. Using only a first order loop filter, the proposed ΔΣ modulator achieves second order noise shaping. Fabricated in a 90-nm CMOS process, the prototype modulator occupies an active area of 0.36 mm<SUP>2</SUP> and consumes 16 mW power. It achieves a peak SNDR of 78.3 dB in 10-MHz bandwidth and an SFDR of better than 85 dB when clocked at 600 MHz. The figure of merit of the modulator is 120 fJ/conv-step.</P>
Energy-Efficient RL-Based Aerial Network Deployment Testbed for Disaster Areas
Ariman, Mehmet,Akkoc, Mertkan,Talip Sari, Tolga,Erol, Muhammed Rasit,Seçinti, Gökhan,Canberk, Berk 한국통신학회 2023 Journal of communications and networks Vol.25 No.1
Rapid deployment of wireless devices with 5G andbeyond enabled a connected world. However, an immediatedemand increase right after a disaster paralyzes network in-frastructure temporarily. The continuous flow of information iscrucial during disaster times to coordinate rescue operations andidentify the survivors. Communication infrastructures built for users of disaster areasshould satisfy rapid deployment, increased coverage, and avail-ability. Unmanned air vehicles (UAV) provide a potential solutionfor rapid deployment as they are not affected by traffic jamsand physical road damage during a disaster. In addition, ad-hocWiFi communication allows the generation of broadcast domainswithin a clear channel which eases one-to-many communications. Moreover, using reinforcement learning (RL) helps reduce thecomputational cost and increases the accuracy of the NP-hardproblem of aerial network deployment. To this end, a novel flying WiFi ad-hoc network managementmodel is proposed in this paper. The model utilizes deep-Q-learning to maintain quality-of-service (QoS), increase userequipment (UE) coverage, and optimize power efficiency. Fur-thermore, a testbed is deployed on Istanbul Technical Univer-sity (ITU) campus to train the developed model. Training resultsof the model using testbed accumulates over 90% packet deliveryratio as QoS, over 97% coverage for the users in flow tables, and0.28 KJ/Bit average power consumption.