Department of Computer Science & Engineering

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IIT Jodhpur

Mobile handoff is a relatively significant charters-tic that involves the quality of connections (QoC) between the base stations (BS) and mobile hosts (MH). Maintaining the QoC in IEEE 802.11 networks is an important challenge in wireless mobile networks, and conjointly the necessity for the different real-life wireless mobile applications. To take care of the QoC, these wireless mobile applications is responsible for fast handoffs between BS. Most of the current research is based on a neighbor graph and maintaining the neighbor table by the connected access point and calculates the scan delay. This paper has proposed a novel neighboring approach for fast handoff where scan delay has been reduced to zero by exploiting the MH neighbor table. The result shows that the proposed approach is better than state-of-the-art approaches in terms of scan delay and re-association delay. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Wireless Personal Communications

An Efficient Algorithm for Fast Handoff in Wireless Mobile Networks

The Internet of Vehicles (IoV) offers an emerging paradigm that deals with interconnected vehicles interacting with the infrastructure, roadside units (RSUs), sensors, and mobile devices with a goal to sense, compute, store, and transmit vital information or data over a common channel while vehicles are moving. Secure and reliable communication and efficient on-device performance are thus crucial challenges in this paradigm, particularly in presence of limited computation resources. This paper presents a novel secure and energy-efficient message communication system, called MComIoV, using a one-way hash function and elliptic curve cryptography (ECC). We evaluate MComIoV through security proof and analysis against various attacks to verify its robustness. The proposed system is also implemented and tested on Raspberry Pi 3B+. Experimental results demonstrate the efficiency in computation time, storage cost, communication overhead, and energy consumption. IEEE

World University root node

IEEE/ACM Transactions on Networking

MComIoV: Secure and Energy-Efficient Message Communication Protocols for Internet of Vehicles

In recent times, approximate computing has found significant use in applications that can tolerate partially inaccurate results. This tolerance can be exploited to design simpler hardware aimed at getting area and energy benefits. In this work, we propose a novel technique to multiply two unsigned binary numbers through a Segmentation based Approximate Multiplier (SAM). The proposed design reduces the size of the Partial Products Matrix (PPM) in the order of n × (2n − 1) to a Reduced Partial Product Matrix (R-PPM) of the order 4 × 2n. Additionally, it also eliminates the extra hardware required for compression and rearrangement of partial products. µ-SAM, an optimized version of our basic design is also proposed along with this work. µ-SAM further minimizes the on-chip area and power consumption of the basic design. The basic design consumes 32.43% lesser on-chip area when compared to the conventional Wallace tree multiplier [1] and produces results that are 89.1% more accurate when compared to other existing state-of-the-art designs such as TOSAM [2], LETAM [3], and DQ4:2C4 [4]. © 2021 IEEE

Proceedings - IEEE International Symposium on Circuits and Systems

SAM: A segmentation based approximate multiplier for error tolerant applications

Approximate computing in recent times has emerged as a popular alternative to conventional computing techniques. Fault-tolerant applications in the domains of machine learning, signal processing, and computer vision have shown promising results using approximate computing. Approximations on adders and multipliers have been widely proposed in literature and innovations on that front are still a necessity so as to target specific applications. In this paper, an approximate carry-lookahead adder (ACLA) is proposed which makes use of an intelligent approach for judging the carry of subsequent stages. Also, a correction mechanism is proposed so as to hinder substantial accuracy loss. Experimental results show that ACLA is faster than the traditional ripple-carry adder by 70.5% for 32-bit configurations on an average. In terms of accuracy, for 32-bit configurations, ACLA outperforms other state-of-the-art adders such as SARA [1] and BCSA [2] by 51%. © 2021 IEEE.

Proceedings - International Symposium on Quality Electronic Design, ISQED

ACLA: An approximate carry-lookahead adder with intelligent carry judgement and correction

Reachability, distance, and matching are some of the most fundamental graph problems that have been of particular interest in dynamic complexity theory in recent years [DKMSZ18, DMVZ18, DKMTVZ20]. Reachability can be maintained with first-order update formulas, or equivalently in DynFO in general graphs with n nodes [DKMSZ18], even under O(log n/loglog n) changes per step [DMVZ18]. In the context of how large the number of changes can be handled, it has recently been shown [DKMTVZ20] that under a polylogarithmic number of changes, reachability is in DynFOpar in planar, bounded treewidth, and related graph classes -- in fact in any graph where small non-zero circulation weights can be computed in NC. We continue this line of investigation and extend the meta-theorem for reachability to distance and bipartite maximum matching with the same bounds. These are amongst the most general classes of graphs known where we can maintain these problems deterministically without using a majority quantifier and even maintain witnesses. For the bipartite matching result, modifying the approach from [FGT], we convert the static non-zero circulation weights to dynamic matching-isolating weights. While reachability is in DynFOar under O(log n/loglog n) changes, no such bound is known for either distance or matching in any non-trivial class of graphs under non-constant changes. We show that, in the same classes of graphs as before, bipartite maximum matching is in DynFOar under O(log n/loglog n) changes per step. En route to showing this we prove that the rank of a matrix can be maintained in DynFOar, also under O(log n/loglog n) entry changes, improving upon the previous O(1) bound [DKMSZ18]. This implies similar extension for the non-uniform DynFO bound for maximum matching in general graphs and an alternate algorithm for maintaining reachability under O(log n/loglog n) changes [DMVZ18].

arXiv preprint arXiv:2109.

Dynamic Meta-theorems for Distance and Matching

In the literature on parameterized graph problems, there has been an increased effort in recent years aimed at exploring novel notions of graph edit-distance that are more powerful than the size of a modulator to a specific graph class. In this line of research, Bulian and Dawar [Algorithmica, 2016] introduced the notion of elimination distance and showed that deciding whether a given graph has elimination distance at most k to any minor-closed class of graphs is fixed-parameter tractable parameterized by k [Algorithmica, 2017]. They showed that Graph Isomorphism parameterized by the elimination distance to bounded degree graphs is fixed-parameter tractable and asked whether determining the elimination distance to the class of bounded degree graphs is fixed-parameter tractable. Recently, Lindermayr et al. [MFCS 2020] obtained a fixed-parameter algorithm for this problem in the special case where the input is restricted to K5-minor free graphs. In this paper, we answer the question of Bulian and Dawar in the affirmative for general graphs. In fact, we give a more general result capturing elimination distance to any graph class characterized by a finite set of graphs as forbidden induced subgraphs. © Akanksha Agrawal, Lawqueen Kanesh, Fahad Panolan, M. S. Ramanujan, and Saket Saurabh; licensed under Creative Commons License CC-BY 4.0.

Leibniz International Proceedings in Informatics, LIPIcs

An FPT algorithm for elimination distance to bounded degree graphs

Given a graph G=(V,E), a subset S⊆V(G) is said to be a feedback vertex set of G if G−S is a forest. In the FEEDBACK VERTEX SET (FVS) problem, we are given an undirected graph G, and a positive integer k, the question is whether there exists a feedback vertex set of size at most k. In this paper, we study three variants of the FVS problem: UNRESTRICTED FAIR FVS, RESTRICTED FAIR FVS, and RELAXED FAIR FVS. In UNRESTRICTED FAIR FVS, we are given a graph G and a positive integer ℓ, the question is does there exist a feedback vertex set S⊆V(G) (of any size) such that for every vertex v∈V(G), v has at most ℓ neighbours in S. First, we study UNRESTRICTED FAIR FVS from different parameterizations such as treewidth, treedepth, and neighbourhood diversity and obtain several results (both tractability and intractability). Next, we study RESTRICTED FAIR FVS, where we are also given an integer k in the input and we demand the size of S to be at most k. This problem is trivially NP-complete; we show that RESTRICTED FAIR FVS when parameterized by the solution size k and the maximum degree Δ of the graph G, admits a kernel of size O(Δk). Finally, we study the RELAXED FAIR FVS problem, where we want that the size of S is at most k and for every vertex v outside S, v has at most ℓ neighbours in S. We give an FPT algorithm for RELAXED FAIR FVS problem running in time cknO(1), for a fixed constant c. © 2021 Elsevier B.V.

Theoretical Computer Science

Parameterized complexity of fair feedback vertex set problem

Image captioning is a widely known problem in the area of AI. Caption generation from floor plan images has applications in indoor path planning, real estate, and providing architectural solutions. Several methods have been explored in the literature for generating captions or semi-structured descriptions from floor plan images. Since only the caption is insufficient to capture fine-grained details, researchers also proposed descriptive paragraphs from images. However, these descriptions have a rigid structure and lack flexibility, making it difficult to use them in real-time scenarios. This paper offers two models, description synthesis from image cue (DSIC) and transformer-based description generation (TBDG), for text generation from floor plan images. These two models take advantage of modern deep neural networks for visual feature extraction and text generation. The difference between both models is in the way they take input from the floor plan image. The DSIC model takes only visual features automatically extracted by a deep neural network, while the TBDG model learns textual captions extracted from input floor plan images with paragraphs. The specific keywords generated in TBDG and understanding them with paragraphs make it more robust in a general floor plan image. Experiments were carried out on a large-scale publicly available dataset and compared with state-of-the-art techniques to show the proposed model’s superiority. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Department of Mathematics

Fulltext

International Journal on Document Analysis and Recognition

Knowledge-driven description synthesis for floor plan interpretation

Reconstruction of time-varying gene regulatory networks underlying a time-series gene expression data is a fundamental challenge in the computational systems biology. The challenge increases multi-fold if the target networks need to be constructed for hundreds to thousands of genes. There have been constant efforts to design an algorithm that can perform the reconstruction task correctly as well as can scale efficiently (with respect to both time and memory) to such a large number of genes. However, the existing algorithms either do not offer time-efficiency, or they offer it at other costs - memory-inefficiency or imposition of a constraint, known as the 'smoothly time-varying assumption'. In this article, two novel algorithms - 'an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators - which is Light on memory' (TGS-Lite) and 'TGS-Lite Plus' (TGS-Lite+) - are proposed that are time-efficient, memory-efficient and do not impose the smoothly time-varying assumption. Additionally, they offer state-of-the-art reconstruction correctness as demonstrated with three benchmark datasets. Source Code: https://github.com/sap01/TGS-Lite-supplem/tree/master/sourcecode. © 2004-2012 IEEE.

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Rapid Reconstruction of Time-varying Gene Regulatory Networks with Limited Main Memory

3D reconstruction of human body model from a monocular image is an under-constrained and challenging yet desired research problem in computer vision. Recently proposed multi-layered shape representation called PeeledHuman attempted a sparse non-parametric 2D representation that can handle severe self-occlusion. However, the key limitation of their PeeledHuman model is that the predicted depth maps of self-occluded parts are sparse and noisy, and hence after back-projection lead to distorted body parts and sometimes with discontinuity between them. In this work, proposed to introduce Peeled Segmentation map representation in a coarse-to-fine refinement framework which consist of a cascade three networks namely, PeelGAN, PSegGAN and RefGAN. At first, we use original PeeledHuman as baseline model to predict initial coarse estimation of peeled depth maps from input RGB image. These peeled maps are subsequently fed as input along with monocular RGB image to our novel PSegGAN which predict Peeled Segmentation maps in a generative fashion. Finally, we feed these peeled segmentation maps as additional context along with monocular input image to our RefGAN which predicts the refined peeled RBG and Depth maps. This also provides an additional output of 3D semantic segmentation of the reconstructed shape. We perform thorough empirical evaluation over four publicly available datasets to demonstrate superiority of our model. © 2021 ACM.