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

Selective and discriminative detection of -NO2 containing high energy organic compounds such as picric acid (PA), 2,4,6-trinitrotoluene (TNT) and dinitrotoluene (DNT) has become a challenging task due to concerns over national security, criminal investigations and environment protections. Among various known detection methods, fluorescence techniques have gained special attention in recent time. A wide variety of fluorescent chemosensors have been developed for nitroaromatic explosive detection. In this review article, we provide an overview of the recent developments made in small molecule-based turn-off fluorescent sensors for nitroaromatic explosives with special focus on organic and H-bonded supramolecular sensors. The fluorescent sensors discussed in this review are classified and organized according to their functionality and their recognition of nitroaromatics by fluorescence quenching. © The Royal Society of Chemistry 2015.

Department of Chemistry

World University root node

Chemical Communications

π-Electron rich small molecule sensors for the recognition of nitroaromatics

This paper presents tracking control laws for two different objectives of a nonholonomic system - a spherical robot - using a geometric approach. The first control law addresses orientation tracking using a modified trace potential function. The second law addresses contact position tracking using a $right$ transport map for the angular velocity error. A special case of this is position and reduced orientation stabilization. Both control laws are coordinate free. The performance of the feedback control laws are demonstrated through simulations.

Department of Electrical Engineering

arXiv preprint arXiv:1511.

Geometric approach to tracking and stabilization for a spherical robot actuated by internal rotors

This paper presents a simple unified ensemble averaged heartbeat extraction framework for noise-robust ECG-based biometric authentication using heartbeat morphology. The proposed method consists of three major steps: preprocessing, ensemble averaged beat construction, and similarity matching. At the preprocessing stage, the signal blocking and mean removal operations are performed. The ensemble averaging stage includes the steps of: discrete cosine transform (DCT) based filter for simultaneous removal of BW and PLI noises, straightforward Gaussian derivative filter (GDF)-based R-peak detector, period normalization and peak-centering, and ensemble averaging computation. At the similarity matching stage, we study the performance of both time-domain and wavelet-domain distance metrics for finding the similarity between a test ensemble ECG beat template and an enrolled ECG beat template. The performance of the proposed framework is tested and validated using different types of ECG signals taken from the standard ECG databases. Results show that the ECG-biometric method using normalized cross correlation (NCC) metric provides consistent verification results as compared to the other four methods under different noisy conditions and sampling rates. For predefined threshold of 0.97, the NCC-based ECG-biometric method achieves an average false rejection rate (FRR) of 11.6% and false acceptance rate (FAR) of 5.8% for 3969 imposters and 19404 test segments from enrolled subjects, respectively. © 2015 IEEE.

2nd International Conference on Signal Processing and Integrated Networks, SPIN 2015

A novel unified framework for noise-robust ECG-based biometric authentication

We report optically sensitive multi-functional metal-insulator-semiconductor devices based on Pt nanoparticles, which were fabricated in-situ with the dielectric stack using atomic layer deposition on a silicon-on-insulator substrate. The devices exhibit non-volatile memory behavior, with a memory window that can be enhanced by optical illumination. Voltage stressed devices serve as highly sensitive broadband photodetectors, as well as optically tunable varactors. © 2015 IEEE.

IEEE-NANO 2015 - 15th International Conference on Nanotechnology

Multi-functional optically sensitive metal-insulator-semiconductor devices based on Pt nanoparticles fabricated in-situ with a dielectric stack using atomic layer deposition

An assessment of electrocardiogram (ECG) signal quality has become an unavoidable first step in most holter and ambulatory ECG signal analysis applications. In this paper, we present a simple method for automatically detection and classification of ECG noises. The proposed method consists of four major steps: moving average filter, blocking, feature extraction, and multistage decision-tree algorithm. In the proposed method, the dynamic amplitude range and autocorrelation maximum peak features are extracted for each block. In the first decision stage, a amplitude-dependent decision rule is used for detecting the presence of low-frequency (LF) noise (including, baseline wander (BW) and abrupt change (ABC) artifacts) and the high-frequency (HF) noise (including, power line interference (PLI) and muscle artifacts). In the second decision stage, the proposed method further classifies the LF noise into a BW noise or a ABC noise using the local dynamic amplitude range feature. The HF noise is classified into a PLI noise or a muscle noise using the local autocorrelation maximum peak feature. The proposed detection and classification method is tested and validated using a wide variety of clean and noisy ECG signals. Results show that the method can achieve an average sensitivity (Se) of 97.88%, positive productivity (+P) of 91.18% and accuracy of 89.06%. © 2015 IEEE.

2015 2nd International Conference on Signal Processing and Integrated …

A simple method for detection and classification of ECG noises for wearable ECG monitoring devices

This study introduces a novel unified framework for simultaneous denoising and compression of electric power system disturbance signals using sparse signal decomposition and reconstruction on overcomplete hybrid dictionary (OHD) matrix. In the proposed method, the power quality signal is first decomposed into deterministic sinusoidal components and non-deterministic components using the OHD matrix, including discrete impulse dictionary (I), cosine dictionary (C), sine dictionary (S) and the l(1)-norm optimisation algorithm. Then, the hard-thresholding, uniform threshold dead-zone quantisation, modified index coding and Huffman coding techniques are used for compression of significant detail signal samples and approximation coefficients. To justify the selection of OHD matrix, four compression methods are implemented using the decomposition techniques based on the dictionaries psi = {[}I C S] and psi = {[}I C], the wavelet transform (WT) and the discrete cosine transform (DCT). The performance of each method is tested and validated using a wide variety of typical power quality disturbance (PQD) signals taken from the IEEE-1159-PQE and GIM-PQE databases and generated using the Microgrid model. The results show that the method with dictionary psi = {[}I C S] is capable of effectively compressing the PQD signals as well as suppressing the noise components in the signals.

IET Generation, Transmission & Distribution

Simultaneous denoising and compression of power system disturbances using sparse representation on overcomplete hybrid dictionaries

Roadways, the links of transportation networks, are susceptible to a variety of degradations. Some degradation patterns are severe; they make the network incapable of meeting origin- destination (OD) demands. Most others are non-severe; they increase the network operational cost while OD demands remain met. In other words, the network structure is resilient to such degradations. It is important for planners to quantify this resilience of a network. The authors have, in a previous study, framed a minimax optimization problem to find the network operational cost— the minimax value—which is irreducible even by re-assignment. The relative difference of the minimax value and the best possible value is interpreted as the network’s resilience. This resilience measure is demand-specific. To generalize the resilience measure, its sensitivity to change in demand should be studied. As it is difficult to analytically establish the sensitivity of resilience to demand, the authors perform computational experiments on different network topologies to bring out a relationship between network resilience and demand. The authors observe that the resilience- demand curve, irrespective of the difference in network size or network topology, follows a trend very much comparable to negative exponential curve. With this result, one needs to compute the demand-specific resilience values at only a given number of demand levels to attain generality. Finally, the authors introduce a generalized index of resilience (GIR) incorporating the demand-spread. The authors compare the GIR to traditional network indicators and find that it is better in modeling topological and dynamic properties of a network.

Department of Civil Engineering

Transportation Research Board 94th Annual Meeting

Investigating the Sensitivity of Road Network Resilience to Demand Variability

Department of Physics

Force-dependent binding of vinculin to alpha-catenin constitutes the core mechanosensing machinery of cadherin adhesions.

The present trend in the manufacturing industry is to make machining processes more environment friendly by adopting topractices such as dry machining, minimum usage of cutting fluid, and usage of non-reactivegas based coolants etc., The concept of Minimum Quantity Lubrication (MQL)has been suggested by several researchers long time back as a cause for addressing the environmental, healthand economic issues related with conventional bulk cooling processes. The MQL technique consists of atomizing a very small quantity of cutting fluid into fine droplets. In other words, the effectiveness of MQL system depends on the quality of these droplets. This paper presents the combination of microscopy and image processing techniques to ascertain the quality of droplets,by spraying the MQL fluidonto an acrylic sheet. The raw droplet features arecaptured with stereo zoom microscopeandanalyzed by measuring the average droplets size and number of droplets per unit area. Reliable MQL application is achieved when the MQL parameters like the distribution of the droplets,the applied carrier gas air pressure, fluid flow rate, nozzle tip to machining zone distance, and the angle at which the nozzle keptis optimal.The optimal MQL conditions as obtained from theseresults have been usedto improve the grindability of Ti-6Al-4V.