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

This paper presents a method of detecting voltage sag, swell and interruption events based on a signal processing tool viz. variational mode decomposition (VMD). The proposed method is able to keep track of variations in amplitude, phase, frequency and undelivered energy due to such events. Voltage signal is half-wave rectified and then decomposed by VMD to get DC part representing instantaneous amplitude envelope, thereby obtaining start/end times and depth of sag/swell. The phase jump and frequency deviations are observed from instantaneous phase, obtained from analytic signal of VMD mode having center frequency nearer to fundamental. Algorithm is tested for various cases of synthetic and simulated signals in MATLAB/Simulink. Comparative performance with recursive discrete Fourier transform and Hilbert transform based method is demonstrated. © 2017 IEEE.

World University root node

Department of Electrical Engineering

Asia-Pacific Power and Energy Engineering Conference, APPEEC

Detection of voltage variation events using variational mode decomposition

Accurate determination of pulse onset and peak is important in many photoplethysmogram (PPG) signal analysis applications. This paper presents a new automatic robust pulse onset and peak detection method which consists of the stationary wavelet transform; the multiscale sum and product; the adaptive amplitude thresholding; the smooth Shannon entropy envelope extraction; the Gaussian derivative filter-based peak finding; and the peak and onset determination and correction. The proposed method achieves an average sensitivity (Se) of 99.66\%, positive predictivity (Pp) of 99.90\%, and overall accuracy (OA) of 99.55\% on a total number of 1 16 255 beats taken from the Massachusetts Institute of Technology-Beth Israel Hospital polysomnographic sleep and complex system laboratory databases and finger pulse database. We further studied the robustness of three peak detection methods such as bandpass filter and Hilbert transform (BPF + HT) {[}12], pulse waveform delineator (PUD) {[}20], and the proposed method using the noisy PPG signals with a signal-to-noise ratio (SNR) ranging from 30 to 10 dB. The method achieved an average Se of 99.51\%, Pp of 99.92\%, and OA of 99.43\% for the PPG signals with SNR of 10 dB whereas the BPF + HT and PUD methods achieved an average Se = 99.66\%, Pp = 87.71\%, and OA = 87.45\%, and Se = 68.04\%, Pp = 70.38\%, and OA = 52.89\%, respectively. The proposed method significantly outperforms the other methods in terms of detection accuracy and robustness under noise-free and noisy PPG recordings. Unlike the existing methods, the proposed method does not use search-back algorithms to reject or include the noise peaks or missed peaks.

IEEE Transactions on Instrumentation and Measurement

A robust pulse onset and peak detection method for automated PPG signal analysis system

This paper proposes a new automated generalized method for detecting an epileptic seizure event from the EEG signals. It consists of preprocessing, variational mode decomposition (VMD), mode sample spectral entropy extraction and thresholding rule. The preprocessing first performs the mean removal and then performs principal component analysis (PCA) based channel selection if required under multi-channel EEG recordings. The VMD is used to decompose the EEG signal into a series of bandlimited modes with specific central frequencies. The mode spectral entropies are extracted from the selected modes by discarding the baseline and artifacts based on the mode central frequencies. The extracted mode spectral entropy features are compared for detecting the presence of the epileptic seizures. The method is evaluated using the standard EEG databases such as Physionet CHB-MIT EEG database and Bonn database. The proposed method achieves an overall accuracy (ACC) of 98-100% with sensitivity (SE) of 100% and specificity (SP) of 97.5-100% for seven EEG groups of the Bonn database. Evaluation results demonstrate that the mode spectral entropy based generic method achieves an average SE = 88.55 %, SP =94.86 % and ACC = 72.92% on the CHB-MIT EEG dataset. © 2018 IEEE.

2018 IEEE 13th International Conference on Industrial and Information …

Detection of epileptic seizure event in EEG signals using variational mode decomposition and mode spectral entropy

A capacitance-to-digital converter (CDC) designed to possess negligible sensitivity to non-ideal circuit parameters is presented in this paper. The scheme is based on switched capacitor integrating technique and the conversion process is such that the final output is fundamentally insensitive to leakage resistance and stray capacitance of the sensor, offset voltage and bias current of the opamp, delay, offset voltage, and bias current of the comparator, leakage current, delay, and charge injection of the switches employed, and so on. Such a characteristic is highly useful for precision measurement. The proposed CDC achieves it without any special compensating circuits, unlike some of the existing CDCs that partially meet the above feature. The performance of the CDC has been experimentally evaluated. Results show high insensitivity to the non-ideal circuit parameters. In addition, the worst case non-linearity error of the prototype CDC was found to be less than 0.1%, when tested for a range of 10 to 400 pF. © 2001-2012 IEEE.

IEEE Sensors Journal

A robust switched-capacitor CDC

Cloud radio access network (C-RAN) and massive multiple-input-multiple-output (MIMO) are two key enabling technologies to meet the diverse and stringent requirements of the 5G use cases. In a C-RAN system with massive MIMO, fronthaul is often the bottleneck due to its finite capacity and transmit precoding is moved to the remote radio head to reduce the capacity requirements on fronthaul. For such a system, we optimize the power allocated to the users to maximize first the weighted sum rate and then the energy efficiency (EE) while explicitly incorporating the capacity constraints on fronthaul. We consider two different fronthaul constraints, which model capacity constraints on different parts of the fronthaul network. We develop successive convex approximation algorithms that achieve a stationary point of these non-convex problems. To this end, we first present novel, locally tight bounds for the user rate expression. They are used to obtain convex approximations of the original non-convex problems, which are then solved by solving their dual problems. In EE maximization, we also employ the Dinkelbach algorithm to handle the fractional form of the objective function. Numerical results show that the proposed algorithms significantly improve the network performance compared to a case with no power control and achieves a better performance than an existing algorithm.

arXiv preprint arXiv:1810.

Power Allocation for Massive MIMO-based, Fronthaul-constrained Cloud RAN Systems

In micro machining processes, achieving the required accuracy and surface quality in the first attempt is of utmost importance. Because, it is challenging to perform a secondary operation in a microscaled structure due to re-positioning inaccuracies. In the micro grinding process, geometrically deviated structures are common due to the low depth of cuts compared to the size of abrasive grits. Moreover, abrasive grits random distribution on the tool surface is also a factor. In most of the cases, the number of grits participating in the grinding process is countable, unlike macro grinding process. Hence, the position of every grit on the micro tool surface is important. In the micro grinding process, geometrically deviated structures are common due to the low depth of cuts compared to the size of abrasive grits. In this case, the depth of cut values will be less than the size of the abrasive grits. In that case, the abrasive grits located at the tool bottom surface highly influence the quality of the component produced. In the present work, a simulation study has been performed for micro grinding process to understand the influence of tool topography on the micro channel geometrical deviations, especially when the depth of cut is less than the size of the grits. Simulation results showed the importance of the number of grits, their protrusion heights and radial positions on the tool bottom surface at small depths of cuts to minimize the geometrical deviations. © Copyright 2018 ASME.

Department of Mechanical Engineering

ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018

Visualization of geometrical deviations in micro grinding by kinematic simulations

Isotopes with low-lying long-lived isomers can behave very differently from other isotopes in astrophysical environments. In particular, the assumption of thermal equilibrium in computing the temperature-dependent β-decay rates of such isotopes can fail below certain temperatures. We focus on the β decay of Al26 since it is one of the most important isotopes in observational astrophysics and has a low-lying isomeric state; we compare and contrast these results with Cl34. We rule out recently reported Al26 effective β-decay rates that showed large differences from previous calculations, finding that we agree with the earlier results. We conclude that in general, effective β-decay rates should be defined separately for the ground and isomeric states at temperatures where thermal equilibrium cannot be achieved. © 2018 American Physical Society.

Department of Physics

Fulltext

Physical Review C

Effective stellar β -decay rates of nuclei with long-lived isomers: Al 26 and Cl 34

In most agricultural systems, the raindrop-induced breakdown of soil aggregates is the initial process of surface soil erosion and redistribution of soil organic matter. The physicochemical differences between the liberated and mobilized material and the residual raindrop-stable soil aggregates can be a critical factor controlling landscape-level heterogeneity in soil biogeochemical reactivity. Using an artificial rainfall simulator with soils from southeastern Iowa, we investigated the role of management intensity on the chemical characteristics of soil particles liberated through raindrop-induced breakdown of both small aggregates (0.25–2 mm; SMAGG) and large aggregates (&gt;2 mm; LGAGG). At all sites LGAGG exhibited lower stability to raindrop energy than SMAGG. Both soil aggregate size classes from a restored prairie and an agricultural site using reduced ridge tillage exhibited higher raindrop stability than conventionally tilled sites. In the restored prairie, the chemical composition (i.e. lignin, substituted fatty acids, SOC and TN, δ15N values) of raindrop-liberated particles was nearly indistinguishable from raindrop-stable aggregates. Among all tilled sites, with the exception of SOC in the conservation tillage site, the raindrop stable particles had relatively higher concentration of measured chemical components versus raindrop-liberated particles. Additionally, the liberated particles in all tilled sites contained higher concentration of oxidized lignin phenols, a lower proportion of cinnamyl to vanillyl lignin, and, as evidenced by the δ15N values, a trend toward a higher proportion of microbially-processed nitrogen, indicating more decomposed microbial processed organic matter. These results are important for understanding the biogeochemical impacts and resulting spatial heterogeneity of raindrop liberated and transported soil particles among landscapes with different management intensity and efforts toward soil conservation. © 2018

Department of Civil Engineering

Geoderma

Control of tillage disturbance on the chemistry and proportion of raindrop-liberated particles from soil aggregates

Post-tensioning systems provide safe and efficient construction solutions for long span bridges. Despite the improved grouting practices over the past decade, existing post-tensioning systems may have significant amount of grout defects, which could lead to corrosion of the strands. Condition assessment of post-tensioning systems is necessary to allow bridge owners to take timely, proactive actions to mitigate or prevent further deterioration and unanticipated tendon failures. A detailed experimental study conducted to assess the performance of nondestructive evaluation techniques in detecting grout defects within internal tendons is presented herein. Nondestructive evaluation techniques that include Ground Penetrating Radar, Impact Echo, Ultrasonic Tomography, and Ultrasonic Echo are evaluated in terms of detecting the location and severity of fabricated grout defects in a full-scale post-tensioned U-girder mock-up specimen. While Ground Penetrating Radar can identify the location and profile of the internal tendons, particularly metal ducts due to strong reflections, this method did not provide any information about the defect conditions within the tendon. Both Impact Echo and Ultrasonic Echo techniques are effective in identifying the location of grout defects, but could not differentiate between water, void, or compromised grout conditions. The study clearly demonstrates the need for NDE techniques that are applicable to anchorage regions, and that are capable of estimating the severity and nature of grout defects in internal tendons. © 2018