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Rohit Singh

Rohit Singh appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Networking and Internet ArchitectureComputer Science and Game Theorycond-mat.mtrl-scieess.SPCryptography and SecurityMachine Learningphysics.opticsProgramming LanguagesArtificial IntelligenceBiological PhysicsBiomoleculescond-mat.mes-hallcond-mat.stat-mechDatabasesEmerging TechnologiesFormal Languages and Automata TheoryLogic in Computer Science

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Published work

16 published item(s)

preprint2026arXiv

Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance

To address the issues of high interruption time and measurement report overhead under user equipment (UE) mobility especially in high speed 5G use cases the use of AI/ML techniques (AI/ML beam management and mobility procedures) have been proposed. These techniques rely heavily on data that are most often simulated for various scenarios and do not accurately reflect real deployment behavior or user traffic patterns. Therefore, there is an utmost need for realistic datasets under various conditions. This work presents a dataset collected from a commercially deployed network across various modes of mobility (pedestrian, bike, car, bus, and train) and at multiple speeds to depict real time UE mobility. When collecting the dataset, we focused primarily on handover (HO) scenarios, with the aim of reducing the HO interruption time and maintaining continuous throughput during and immediately after HO execution. To support this research, the dataset includes timing advance (TA) measurements at various signaling events such as RACH trigger, MAC CE, and PDCCH grant which are typically missing in existing works. We cover a detailed description of the creation of the dataset; experimental setup, data acquisition, and extraction. We also cover an exploratory analysis of the data, with a primary focus on mobility, beam management, and TA. We discuss multiple use cases in which the proposed dataset can facilitate understanding of the inference of the AI/ML model. One such use case is to train and evaluate various AI/ML models for TA prediction.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Investigating the impact of BTI, HCI and time-zero variability on neuromorphic spike event generation circuits

Neuromorphic computing refers to brain-inspired computers, that differentiate it from von Neumann architecture. Analog VLSI based neuromorphic circuits is a current research interest. Two simpler spiking integrate and fire neuron model namely axon-Hillock (AH) and voltage integrate, and fire (VIF) circuits are commonly used for generating spike events. This paper discusses the impact of reliability issues like Bias Temperature instability (BTI) and Hot Carrier Injection (HCI), and timezero variability on these CMOS based neuromorphic circuits. AH and VIF circuits are implemented using HKMG based 45nm technology. For reliability analysis, industry standard Cadence RelXpert tool is used. For time-zero variability analysis, 1000 Monte-Carlo simulations are performed.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Consenting to Internet of Things Across Different Social Settings

Devices connected to the Internet of Things (IoT) are rapidly becoming ubiquitous across modern homes, workplaces, and other social environments. While these devices provide users with extensive functionality, they pose significant privacy concerns due to difficulties in consenting to these devices. In this work, we present the results of a pilot study that shows how users consent to devices in common locations at a friends house in which the user is a guest attending a party. We use this pilot study to indicate a direction for a larger study, which will capture a more granular understanding of how users will consent to a variety of devices placed in different social settings (i.e. a party house owned by a friend, an office space for the user and some 40 other employees, the bathroom of a department store). Our final contribution of this work will be to build a probability distribution which will indicate how probable a given user is to consent to a device given what sensors it has, where it is, and the awareness and preferences of each user.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An Analytical Model for Efficient Indoor THz Access Point Deployment

Ultra-densification of user equipment (UE) and access points (APs) are anticipated to take a toll on the future spectrum needs. Higher frequency bands, such as mmWave ($30$-$300GHz$) and THz spectrum ($0.3$-$10THz$), can be used to cater to the high-throughput needs of ultra-dense networks. These high-frequency bands have a tremendous amount of \textit{green-filed contiguous spectrum}, ranging in hundreds of $GHz$. However, these bands, especially the THz bands, face numerous challenges, such as high spreading, absorption, and penetration losses. To combat these challenges, the THz-APs need to be either equipped with high transmit power, high antenna gains (i.e., narrow antenna beams), or limit the communication to short-ranges. All of these factors are bounded due to technical or economic challenges, which will result in a \textit{"distance-power dilemma"} while deciding on the deployment strategy of THz-APs. In this paper, we present an analytical model to deploy THz-APs in an indoor setting efficiently. We further show through extensive numerical analysis, the optimal number of APs and optimal room length for different blocks of the THz spectrum. Furthermore, these THz-APs need to be efficiently packed to avoid outages due to handoffs, which can add more complexity to the dilemma. To mitigate the packing problem, we propose two solutions over the optimal solution: (a) Radius Increase, and (b) Repeater Assistance, and present an analytical model for each.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Green Security Game with Community Engagement

While game-theoretic models and algorithms have been developed to combat illegal activities, such as poaching and over-fishing, in green security domains, none of the existing work considers the crucial aspect of community engagement: community members are recruited by law enforcement as informants and can provide valuable tips, e.g., the location of ongoing illegal activities, to assist patrols. We fill this gap and (i) introduce a novel two-stage security game model for community engagement, with a bipartite graph representing the informant-attacker social network and a level-$κ$ response model for attackers inspired by cognitive hierarchy; (ii) provide complexity results and exact, approximate, and heuristic algorithms for selecting informants and allocating patrollers against level-$κ$ ($κ<\infty$) attackers; (iii) provide a novel algorithm to find the optimal defender strategy against level-$\infty$ attackers, which converts the problem of optimizing a parameterized fixed-point to a bi-level optimization problem, where the inner level is just a linear program, and the outer level has only a linear number of variables and a single linear constraint. We also evaluate the algorithms through extensive experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

SHINE (Strategies for High-frequency INdoor Environments) with Efficient THz-AP Placement

The increasing demand for ultra-high throughput in ultra-dense networks might take a toll on 5G capacity. Moreover, with Internet-of-Things (IoT) and the growing use-cases for indoor killer-applications, it will be necessary to look beyond 5G technologies. One such promising technology is to move higher in the frequencies, such as the THz ($300$ $GHz$-$10$ $THz$) spectrum. THz has a massive number of greenfield-contiguous channels ranging from $10$ $GHz$ to $200$ $GHz$ (best case), which was not available in the traditional radio frequency (RF) or millimeter wave (mmWave) bands. Although THz has immense potential to cater to such demands, it comes with numerous challenges revolving around hardware, link budget, mobility, blockages, scheduling, and deployment. Terahertz access points (THz-APs) are sensitive to deployment and can critically impact a system\textquotesingle s dynamics (i.e., coverage, throughput, and efficiency). In this paper, we present Strategies for High-frequency INdoor Environments or SHINE, which focuses on efficient AP deployment in the THz spectrum and draws motivation from approaches used indoor to improve lighting conditions. Due to THz\textquotesingle s limited coverage area of a few meters, the number of THz-APs required to satisfy a densely populated room will be higher compared to today's single router/box/AP model. This increased number of THz-APs will not only increase the operational costs, but also (in some cases) can make the system inefficient. Through SHINE, we explore the deployment-related challenges and propose strategies to mitigate the same.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Ultra-dense Low Data Rate (UDLD) Communication in the THz

In the future, with the advent of Internet of Things (IoT), wireless sensors, and multiple 5G killer applications, an indoor room might be filled with $1000$s of devices demanding low data rates. Such high-level densification and mobility of these devices will overwhelm the system and result in higher interference, frequent outages, and lower coverage. The THz band has a massive amount of greenfield spectrum to cater to this dense-indoor deployment. However, a limited coverage range of the THz will require networks to have more infrastructure and depend on non-line-of-sight (NLOS) type communication. This form of communication might not be profitable for network operators and can even result in inefficient resource utilization for devices demanding low data rates. Using distributed device-to-device (D2D) communication in the THz, we can cater to these Ultra-dense Low Data Rate (UDLD) type applications. D2D in THz can be challenging, but with opportunistic allocation and smart learning algorithms, these challenges can be mitigated. We propose a 2-Layered distributed D2D model, where devices use coordinated multi-agent reinforcement learning (MARL) to maximize efficiency and user coverage for dense-indoor deployment. We show that densification and mobility in a network can be used to further the limited coverage range of THz devices, without the need for extra infrastructure or resources.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Automatic Generation of Formula Simplifiers based on Conditional Rewrite Rules

This paper addresses the problem of creating simplifiers for logic formulas based on conditional term rewriting. In particular, the paper focuses on a program synthesis application where formula simplifications have been shown to have a significant impact. We show that by combining machine learning techniques with constraint-based synthesis, it is possible to synthesize a formula simplifier fully automatically from a corpus of representative problems, making it possible to create formula simplifiers tailored to specific problem domains. We demonstrate the benefits of our approach for synthesis benchmarks from the SyGuS competition and automated grading.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Reply to Comment on Nonlocal quartic interactions and universality classes in perovskite manganites

Comment [arXiv:cond-mat.stat.mech., 1602.02087v1 (2016)] has raised questions claiming that the nonlocal model Hamiltonian presented in [Phys. Rev. E 92, 012123 (2015)] is equivalent to the standard (short-ranged) Φ^4 theory. These claims are based on a low momentum expansion of the interaction vertex that cannot be applied to the vertex factors containing both low and high momenta inside the loop-integrals. Elaborating upon the important steps of the momentum shell decimation scheme, employed in the renormalization-group calculation, we explicitly show the interplay of internal (high) and external (low) momenta determining the loop integrals for self-energy and vertex functions giving rise to corrections (to the bare parameters) different from those of the standard (short-ranged) Φ^4 theory. Employing explicit mathematical arguments, we show that this difference persists when the range of interaction is assumed to be long (short) ranged with respect to the lattice constant (correlation-length), yielding the critical exponents as given in the original paper.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

A Tunable Terahertz Detector Based On Self Assembled Plasmonic Structure on a GaAs 2DEG

Plasmonic detectors have the potential to provide a method of rapid spectroscopy without the need of moving mirrors or gratings. Previous measurements have demonstrated frequency tunable detection based on plasmonic excitations, however these devices were either small area, polarization dependent and/or required e-beam lithography. We demonstrate that large area high sensitivity THz plasmonic detection can be achieved using self-assembly nanosphere lithography. We achieve a submicron feature size grid covering a detector area of 4 mm^2. The absorbance at the fundamental, third and 5th harmonics of plasmon resonances are observed. Measurements at 80 K show a large transmission change of 25%. We also confirmed a magneto plasmon dispersion of this device. In this paper we will discuss the frequency dependence of the radiative damping effect which affects enhanced absorption at the higher harmonics mode relative to fundamental.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Data Hiding in Binary Image using Block Parity

Secret data hiding in binary images is more difficult than other formats since binary images require only one bit representation to indicate black and white. This study proposes a new method for data hiding in binary images using optimized bit position to replace a secret bit. This method manipulates blocks, which are sub-divided. The parity bit for a specified block decides whether to change or not, to embed a secret bit. By finding the best position to insert a secret bit for each divided block, the image quality of the resulting stego-image can be improved, while maintaining low computational complexity.The experimental results show that the proposed method has an improvement with respect to a previous work.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Understanding the metamagnetic transition and magnetic behavior of Ni48Co6Mn26Al20 polycrystalline ribbons

In this work we demonstrate that the polycrystalline ribbons of (Ni48Co6)Mn26Al20 with B2 structure at room temperature show a magnetic behavior with competing magnetic exchange interactions leading to frozen disorders at low temperatures. It is established that by considering the presence of both antiferromagnetic and ferromagnetic sublattices, we can explain the observed magnetic behavior including the metamagnetic transition observed in these samples. From the Arrott plots, the Néel temperature of (Ni48Co6)Mn26Al20 is deduced to be ~170 K and the broad ferro to para like magnetic phase transition is observed at ~ 200 K. Based on Néel theory, a cluster model is used to explain the presence of ferromagnetic and anti-ferromagnetic clusters in the studied ribbons. Formation of ferromagnetic clusters can be understood in terms of positive exchange interactions among the Mn atoms that are neighboring to Co atoms which are located on the Ni sites.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Measuring and Synthesizing Systems in Probabilistic Environments

Often one has a preference order among the different systems that satisfy a given specification. Under a probabilistic assumption about the possible inputs, such a preference order is naturally expressed by a weighted automaton, which assigns to each word a value, such that a system is preferred if it generates a higher expected value. We solve the following optimal-synthesis problem: given an omega-regular specification, a Markov chain that describes the distribution of inputs, and a weighted automaton that measures how well a system satisfies the given specification under the given input assumption, synthesize a system that optimizes the measured value. For safety specifications and measures given by mean-payoff automata, the optimal-synthesis problem amounts to finding a strategy in a Markov decision process (MDP) that is optimal for a long-run average reward objective, which can be done in polynomial time. For general omega-regular specifications, the solution rests on a new, polynomial-time algorithm for computing optimal strategies in MDPs with mean-payoff parity objectives. Our algorithm generates optimal strategies consisting of two memoryless strategies and a counter. This counter is in general not bounded. To obtain a finite-state system, we show how to construct an ε-optimal strategy with a bounded counter for any ε>0. We also show how to decide in polynomial time if we can construct an optimal finite-state system (i.e., a system without a counter) for a given specification. We have implemented our approach in a tool that takes qualitative and quantitative specifications and automatically constructs a system that satisfies the qualitative specification and optimizes the quantitative specification, if such a system exists. We present experimental results showing optimal systems that were generated in this way.

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preprint2011arXiv

On Memoryless Quantitative Objectives

In two-player games on graph, the players construct an infinite path through the game graph and get a reward computed by a payoff function over infinite paths. Over weighted graphs, the typical and most studied payoff functions compute the limit-average or the discounted sum of the rewards along the path. Beside their simple definition, these two payoff functions enjoy the property that memoryless optimal strategies always exist. In an attempt to construct other simple payoff functions, we define a class of payoff functions which compute an (infinite) weighted average of the rewards. This new class contains both the limit-average and discounted sum functions, and we show that they are the only members of this class which induce memoryless optimal strategies, showing that there is essentially no other simple payoff functions.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Quantitative Synthesis for Concurrent Programs

We present an algorithmic method for the quantitative, performance-aware synthesis of concurrent programs. The input consists of a nondeterministic partial program and of a parametric performance model. The nondeterminism allows the programmer to omit which (if any) synchronization construct is used at a particular program location. The performance model, specified as a weighted automaton, can capture system architectures by assigning different costs to actions such as locking, context switching, and memory and cache accesses. The quantitative synthesis problem is to automatically resolve the nondeterminism of the partial program so that both correctness is guaranteed and performance is optimal. As is standard for shared memory concurrency, correctness is formalized "specification free", in particular as race freedom or deadlock freedom. For worst-case (average-case) performance, we show that the problem can be reduced to 2-player graph games (with probabilistic transitions) with quantitative objectives. While we show, using game-theoretic methods, that the synthesis problem is NEXP-complete, we present an algorithmic method and an implementation that works efficiently for concurrent programs and performance models of practical interest. We have implemented a prototype tool and used it to synthesize finite-state concurrent programs that exhibit different programming patterns, for several performance models representing different architectures.

0 citations0 reviewsNo code linkedOpen access