Samir R. Das

Bio: Samir R. Das is an academic researcher from Stony Brook University. The author has contributed to research in topics: Wireless network & Physics. The author has an hindex of 58, co-authored 186 publications receiving 29007 citations. Previous affiliations of Samir R. Das include University of Texas at San Antonio & University of Cincinnati.

Production of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msup><mml:mi>K</mml:mi><mml:mo>*</mml:mo></mml:msup><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mn>892</mml:mn><mml:mo>)</mml:mo></mml:mrow><mml:mn>0</mml:mn></mml:msup></mml:mrow></mml:math> and <mml:math xmlns:

Abstract: The production of K*(892)0 and ϕ(1020) mesons in proton–proton (pp) and lead–lead (Pb–Pb) collisions at sNN = 5.02 TeV has been measured using the ALICE detector at the Large Hadron Collider (LHC). The transverse momentum (pT) distributions of K*(892)0 and ϕ(1020) mesons have been measured at midrapidity (|y|<0.5) up to pT= 20 GeV/c in inelastic pp collisions and for several Pb–Pb collision centralities. The collision centrality and collision energy dependence of the average transverse momenta agree with the radial flow scenario observed with stable hadrons, showing that the effect is stronger for more central collisions and higher collision energies. The K*0/K ratio is found to be suppressed in Pb–Pb collisions relative to pp collisions: this indicates a loss of the measured K*(892)0 signal due to rescattering of its decay products in the hadronic phase. In contrast, for the longer-lived ϕ(1020) mesons, no such suppression is observed. The nuclear modification factors (RAA) of K*(892)0 and ϕ(1020) mesons are calculated using pp reference spectra at the same collision energy. In central Pb–Pb collisions for pT > 8 GeV/c, the RAA values of K*(892)0 and ϕ(1020) are below unity and observed to be similar to those of pions, kaons, and (anti)protons. The RAA values at high pT (>8 GeV/c) for K*(892)0 and ϕ(1020) mesons are in agreement within uncertainties for sNN= 5.02 and 2.76 TeV.8 MoreReceived 10 July 2021Accepted 19 August 2022DOI:https://doi.org/10.1103/PhysRevC.106.034907Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.©2022 CERN, for the ALICE CollaborationPhysics Subject Headings (PhySH)Research AreasHadron-hadron interactionsNucleon induced nuclear reactionsParticle & resonance productionRelativistic heavy-ion collisionsParticles & FieldsNuclear Physics

Radio environment mapping with mobile devices in the TV white space

Abstract: In this paper, we envision a scenario where mobile devices perform at least part-time spectrum sensing in a collaborative fashion under the control of a central server. The goal is to create an adequate `radio environment map' for the `white spaces' that will be useful for spectrum management decisions. We lay out the research challenges, describe a prototype implementation using a DTV receiver dongle interfaced with an Android-based mobile device, and present preliminary performance measurements.

Collaborative Channel Estimation in Backscattering Tag-to-Tag Networks

Abstract: Backscattering Tag-to-Tag Networking (BTTN) represents a rapidly emerging paradigm enabling passive, radio-less tags to communicate directly with each other by reflecting (backscattering) an RF signal supplied by an external un-coordinated exciter. Recent advancements have taken the capability of BTTN beyond basic communication, empowering the networks with the ability to collaboratively sense and recognize human activities in the deployment space. The key to this ability is a novel passive channel estimation, allowing individual tags to measure tag-to-tag wireless channel parameters without involvement of any active radio. Previously reported techniques for this suffer from a limitation in that, they are unable to isolate the tag-to-tag channel of interest from the wider-range exciter-to-tag channels. As a result, the channel estimates and the analytics based thereof are susceptible to dynamic variations and clutter in the overall deployment environment, outside the range of the tag-to-tag link. In this paper, we overcome these limitations using a novel collaborative technique thus greatly enhancing the utility of passive channel estimation in BTTN. We elucidate our proposed technique using analytical modeling and validate with in-lab experiments using tag hardware built from discrete components.

Swift: Adaptive Video Streaming with Layered Neural Codecs

Abstract: Layered video coding compresses video segments into layers (additional code bits). Decoding with each additional layer improves video quality incrementally. This approach has po-tential for very fine-grained rate adaptation. However, layered coding has not seen much success in practice because of its cross-layer compression overheads and decoding latencies. We take a fresh new approach to layered video coding by exploiting recent advances in video coding using deep learning techniques. We develop Swift , an adaptive video streaming system that includes i) a layered encoder that learns to encode a video frame into layered codes by purely encoding residuals from previous layers without introducing any cross-layer compression overheads, ii) a decoder that can fuse together a subset of these codes (based on availability) and decode them all in one go, and, iii) an adaptive bit rate (ABR) protocol that synergistically adapts video quality based on available network and client-side compute capacity. Swift can be integrated easily in the current streaming ecosystem without any change to network protocols and applications by simply replacing the current codecs with the proposed layered neural video codec when appropriate GPU or similar accelerator functionality is available on the client side. Extensive evaluations reveal Swift ’s multi-dimensional benefits over prior video streaming systems.

Hypertriton Production in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>p</mml:mi></mml:math> -Pb Collisions at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msqrt><mml:mrow><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:m

Abstract: The study of nuclei and antinuclei production has proven to be a powerful tool to investigate the formation mechanism of loosely bound states in high-energy hadronic collisions. The first measurement of the production of _{Λ}^{3}H in p-Pb collisions at sqrt[s_{NN}]=5.02 TeV is presented in this Letter. Its production yield measured in the rapidity interval -1

An application-specific protocol architecture for wireless microsensor networks

Abstract: Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.

Directed diffusion: a scalable and robust communication paradigm for sensor networks

Abstract: Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.

Epidemic routing for partially-connected ad hoc networks

Abstract: Mobile ad hoc routing protocols allow nodes with wireless adaptors to communicate with one another without any pre-existing network infrastructure. Existing ad hoc routing protocols, while robust to rapidly changing network topology, assume the presence of a connected path from source to destination. Given power limitations, the advent of short-range wireless networks, and the wide physical conditions over which ad hoc networks must be deployed, in some scenarios it is likely that this assumption is invalid. In this work, we develop techniques to deliver messages in the case where there is never a connected path from source to destination or when a network partition exists at the time a message is originated. To this end, we introduce Epidemic Routing, where random pair-wise exchanges of messages among mobile hosts ensure eventual message delivery. The goals of Epidemic Routing are to: i) maximize message delivery rate, ii) minimize message latency, and iii) minimize the total resources consumed in message delivery. Through an implementation in the Monarch simulator, we show that Epidemic Routing achieves eventual delivery of 100% of messages with reasonable aggregate resource consumption in a number of interesting scenarios.