Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

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IIT Palakkad

World University root node

Department of Physics

Scientific Reports

Author Correction: A Low-Cost Non-explosive Synthesis of Graphene Oxide for Scalable Applications

Calcium carbide residue and fly ash are industrial by-products from acetylene gas industry and thermal power stations. Pavement construction has been recognized as a platform to utilize huge quantities of these waste materials for soil stabilization. An attempt has been made, in this paper, to evaluate the potential of a binder that consists of calcium carbide residue and fly ash to improve the engineering behavior of an expansive soil. The role of curing period and different percentages of these binder contents on Atterberg limits, compaction characteristics, strength, and mineralogical and morphological behavior has been examined. The index properties such as Atterberg limits and compaction characteristics have improved considerably. Significant improvement in the strength properties such as unconfined compressive strength and California bearing ratio has been observed with the addition of calcium carbide residue up to 8%. The binder content prepared with 10% calcium carbide residue and 6% fly ash obtained 18-fold enhancement in California Bearing Ratio (CBR) value and 16-fold enhancement in unconfined compressive strength (UCS). This is attributed to the formation of calcium based minerals formed as a result of a chemical reaction between the soil and binder, which were evidenced from mineralogical and morphological studies. From this experimental investigation, it can be concluded that the binder prepared with calcium carbide residue and fly ash is well suited for stabilization of expansive soils. © 2018 University of Kuwait. All rights reserved.

Department of Civil Engineering

Journal of Engineering Research

Multi-scale laboratory investigation on black cotton soils stabilized with calcium carbide residue and fly ash

Magnetic insulators are a key resource for next-generation spintronic and topological devices. The family of layered metal halides promises varied magnetic states, including ultrathin insulating multiferroics, spin liquids, and ferromagnets, but device-oriented characterization methods are needed to unlock their potential. Here, we report tunneling through the layered magnetic insulator CrI3 as a function of temperature and applied magnetic field.We electrically detect the magnetic ground state and interlayer coupling and observe a fieldinducedmetamagnetic transition.The metamagnetic transition results in magnetoresistances of 95, 300, and 550% for bilayer, trilayer, and tetralayer CrI3 barriers, respectively.We further measure inelastic tunneling spectra for our junctions, unveiling a rich spectrum consistent with collective magnetic excitations (magnons) in CrI3. © 2018 American Association for the Advancement of Science. All Rights Reserved.

Fulltext

Science

Probing magnetism in 2D van der Waals crystalline insulators via electron tunneling

Department of Data Science

2018‏

Architecture and Programming Model Support for Reconfigurable Accelerators in Multi-Core Embedded Systems‏

The physically based distributed hydrological models are ideal for hydrological simulations; however most of such models do not use the basic equations pertaining to mass, energy and momentum conservation, to represent the physics of the process. This is plausibly due to the lack of complete understanding of the hydrological process. The soil and water assessment tool (SWAT) is one such widely accepted semi-distributed, conceptual hydrological model used for water resources planning. However, the over-parameterization, difficulty in its calibration process and the uncertainty associated with predictions make its applications skeptical. This study considers assessing the predictive uncertainty associated with distributed hydrological models. The existing methods for uncertainty estimation demand high computational time and therefore make them challenging to apply on complex hydrological models. The proposed approach employs the concepts of generalized likelihood uncertainty estimation (GLUE) in an iterative procedure by starting with an assumed prior probability distribution of parameters, and by using mutual information (MI) index for sampling the behavioral parameter set. The distributions are conditioned on the observed information through successive cycles of simulations. During each cycle of simulation, MI is used in conjunction with Markov Chain Monte Carlo procedure to sample the parameter sets so as to increase the number of behavioral sets, which in turn helps reduce the number of cycles/simulations for the analysis. The method is demonstrated through a case study of SWAT model in Illinois River basin in the USA. A comparison of the proposed method with GLUE indicates that the computational requirement of uncertainty analysis is considerably reduced in the proposed approach. It is also noted that the model prediction band, derived using the proposed method, is more effective compared to that derived using the other methods considered in this study. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

Stochastic Environmental Research and Risk Assessment

A computationally efficient method for uncertainty analysis of SWAT model simulations

Department of Mechanical Engineering

Pre-prints of the National Meeting of Japan Welding Society

Effect of plate thickness on tool life in FSW of high strength aluminum alloy

Real-world phenomena are often partially observed. This partial observability leads to incomplete data. Acquiring more data is often expensive, hard, or impossible. We present a feasibility study on the limits of online learning to reduce incompleteness in network data. In particular, we investigate the following problem: given a network and limited resources to collect more data (i.e., a budget), can an optimal strategy be learned for reducing the network's in-completeness? Reducing the incompleteness of a network can be interpreted in different ways. For example, it could mean: observe as many previously unobserved nodes as possible, or observe as many new nodes with a certain property, or observe as many new nodes and triangles, etc. Here, we focus on the first interpretation-i.e., we use the given budget to increase the number of nodes in the incomplete graph. Using one unit of the budget means querying an oracle that has access to the fully observed network and getting back full information about a single node's neighbors (at the time of query). We refer to this process as probing a node. Examples of probing nodes include using APIs to get information about an account, placing monitors on routers to get information about Inter-net traffic flow, etc. We make no assumptions about the underlying model generating the network data or how the incomplete network was observed. Our findings on synthetic and real-world networks showcase when learning is feasible, when it is not and when one should just use a heuristic (i.e., when learning is unnecessary or redundant).

The 14th international workshop on mining and learning with graphs

Reducing Network Incompleteness Through Online Learning: A Feasibility Study

Electrical discharge machining (EDM) is a non-contact machining process that removes material through spark erosion and is often used for drilling holes in difficult-to-cut materials such as Nickel-based super alloys. However, this process becomes slower with the increase of the hole depth. This is because the flushing pressure becomes inefficient for the effective evacuation of process debris leading to secondary discharges and resulting in increased machining time. This study presents an innovative simultaneous flushing and vacuum-assisted debris removal system, which facilitates better debris removal for deep-hole EDM drilling. Improvement in the drilling time and better surface roughness have been achieved using the developed setup. To evaluate the performance of the vacuum-assisted debris removal system, a novel computational fluid dynamics model is also proposed in this study. Experimentally measured particle size data of full length scale act as a reference for the proposed numerical model.The analysis presented in this study provides significant insight into sizes of the debris particles in different machining conditions, in full length scale using an automated image processing technique. The presented numerical model can be used to investigate the various factors influencing the removal of debris from the machining zone. The experimental and numerical components of this research complement each other in the design of the presented vacuum-assisted debris removal system. © 2017 Elsevier B.V.

Journal of Materials Processing Technology

A study on EDM debris particle size and flushing mechanism for efficient debris removal in EDM-drilling of Inconel 718

88th Annual Session of The National Academy of Sciences, India

IMPORTANCE OF STRENGTH ANISOTROPY IN NUMERICAL ANALYSIS

We consider the problem of distributed scheduling in wireless communication networks where heterogeneously delayed queue lengths and channel states of all links are available at all the transmitters. In an earlier work (by Reddy et al. in Queueing Systems, 2012), a throughput-optimal scheduling policy (which we refer to henceforth as the R policy) for this setting was proposed. We study the R policy, and examine its two drawbacks — (i) its huge computational complexity, and (ii) its non-optimal average per-packet queueing delay. We show that the R policy unnecessarily constrains itself to work with information that is more delayed than that afforded by the system. We propose a new distributed scheduling policy that fully exploits the common state information available to all transmitters, thereby greatly improving upon the computational complexity and the delay performance relative to those of the R policy. We also establish the throughput optimality of our policy analytically. We evaluate the performance of the proposed policy and validate our analytical results through extensive numerical simulation. Thus, our work enlarges the ambit of networks for which throughput-optimal scheduling is practicable. © 2018 Elsevier B.V.

Department of Computer Science and Engineering

Performance Evaluation

Reduced-complexity delay-efficient throughput-optimal distributed scheduling with heterogeneously delayed network-state information