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

The concurrent control of spacecraft equipped with one-axis unilateral thruster and three-axis attitude actuator is considered in this paper. The proposed control law utilizes attitude control channels along with the single thrust force concurrently, for three-dimensional trajectory tracking and rendezvous with a target object. The concurrent controller also achieves orbital transfer to low Earth orbits with long range separation. To demonstrate the orbit transfer capabilities of the concurrent controller, a smooth elliptical orbit transfer trajectory for co-planar circular orbits is designed. The velocity change and energy consumption of the designed orbit transfer trajectory is observed to be equivalent to that of Hohmann transfer. © 2017 IAA

Department of Electrical Engineering

World University root node

Acta Astronautica

Concurrent rendezvous control of underactuated spacecraft

Background: Intravenous immunoglobulin (IVIG) is a polyspecific pooled immunoglobulin G preparation and one of the commonly used therapeutics for autoimmune diseases including those of neurological origin. A recent report in murine model proposed that IVIG expands regulatory T (Treg) cells via induction of interleukin 33 (IL-33). However, translational insight on these observations is lacking. Methods: Ten newly diagnosed Guillain-Barré syndrome (GBS) patients were treated with IVIG at the rate of 0.4 g/kg for three to five consecutive days. Clinical evaluation for muscular weakness was performed by Medical Research Council (MRC) and modified Rankin scoring (MRS) system. Heparinized blood samples were collected before and 1, 2, and 4-5 weeks post-IVIG therapy. Peripheral blood mononuclear cells were stained for surface CD4 and intracellular Foxp3, IFN-γ, and tumor necrosis factor alpha (TNF-α) and were analyzed by flow cytometry. IL-33 and prostaglandin E2 in the plasma were measured by ELISA. Results: The fold changes in plasma IL-33 at week 1 showed no correlation with the MRC and MRS scores at weeks 1, 2, and ≥4 post-IVIG therapy. Clinical recovery following IVIG therapy appears to be associated with Treg cell response. Contrary to murine study, there was no association between the fold changes in IL-33 at week 1 and Treg cell frequency at weeks 1, 2, and ≥4 post-IVIG therapy. Treg cell-mediated clinical response to IVIG therapy in GBS patients was associated with reciprocal regulation of effector T cells-expressing TNF-α. Conclusion: Treg cell expansion by IVIG in patients with autoimmune diseases lack correlation with IL-33. Treg cell frequency, but not plasma IL-33 levels, represents potential immunological biomarker to predict clinical response to IVIG therapy. © 2017 The Author(s).

Biological Sciences and Engineering

Fulltext

Journal of Neuroinflammation

Regulatory T cell frequency, but not plasma IL-33 levels, represents potential immunological biomarker to predict clinical response to intravenous immunoglobulin therapy

EUROPEAN BIOPHYSICS JOURNAL WITH BIOPHYSICS LETTERS

Functional amyloids from the fungal pathogen Aspergillus fumigatus

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal. © 2017, Institution of Engineering and Technology. All rights reserved.

Healthcare technology letters

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal

In this paper, we address the problem of active noise cancellation in a spatial region in presence of outliers in secondary paths and impulse-like noises encountered in the error microphones due to malfunctioning or failure of the sensors, errors in measurement and movement of microphone or loudspeaker. In conventional multiple error filtered x least mean square (MEFxLMS) algorithm based multi-channel active noise control (MANC) system, such disturbances result in increase in residual noise leading to divergence of the weights of the controller. Furthermore, the traditional MANC techniques are not scalable and computationally complex when the noise cancellation is desired in a wide area. With an objective to mitigate noise in such an environment, acoustic nodes equipped with ANC modules are deployed in the area. A robust distributed active noise control system (RDANC) is developed based on incremental cooperative learning among these sensor nodes and R-estimator based robust cost function. The performance of the proposed system is compared with conventional block-MANC system and incremental block-DANC system for different strengths of outliers and disturbances. Evaluation results show that the proposed system is robust to disturbances in secondary paths and error sensors as compared to block-MANC and block-DANC systems. © 2017 IEEE.

TENCON 2017-2017 IEEE Region 10 Conference

Robust distributed active noise control in presence of secondary path and error sensor disturbances

Accurate determination of glottal instants and electroglottographic (EGG) parameters is most important in voice pathology analysis including multiple voice disorders: neurological, functional, and laryngeal diseases. In this paper, we present a new effective method for reliable detection of glottal instants and EGG parameters from an EGG signal composed of voiced and nonvoice segments. In the first stage, we present an adaptive variational mode decomposition based algorithm for suppressing low-frequency artifacts and additive high-frequency noises. Based upon mode center frequency criterion, the proposed method first constructs a candidate EGG feature signal for determination of glottal closure and opening instants. In the second stage, the candidate glottal instants are determined by detecting the positive and negative zerocrossings in normalized candidate EGG feature signal, respectively. Finally, an autocorrelation features based postprocessing algorithm is presented to reject nonglottal instants from the nonspeech production segments. The accuracy and robustness of the method is tested using noise-free and noisy EGG signals. Evaluation results show that the proposed method achieves an average overall accuracy of 95.06%, identification rate of 95.34%, missed rate of 3.60%, and false alarm rate of 0.06% with average absolute identification error of 0.71 ± 0.66 ms for an SNR of 15 dB. Results demonstrate that the proposed method significantly outperforms the other existing methods under both noise-free and noisy EGG signals. © 2013 IEEE.

IEEE journal of biomedical and health informatics

Effective glottal instant detection and electroglottographic parameter extraction for automated voice pathology assessment

In this paper, we propose a new automated method for determining systolic peaks in the photoplethysmography(PPG) signal. The proposed systolic peak detection method includes four stages. In the first stage, variational mode decomposition (VMD) technique is used for enhancing the PPG signal and suppression of background noises such as baseline wanders, powerline and high-frequency noises. In the second stage, the candidate envelope is obtained using a center of gravity (CoG) technique. In the third stage, the locations of peaks present in the local waves of the envelope are determined by detecting the positive zerocrossing points of the CoG envelope. Finally, the detected peak locations are used for finding true locations of systolic peaks present in the PPG signal using the peak adjustment procedure. The proposed method is validated and tested by using variety of PPG signals. Evaluation results showed that the method achieves an average Se of 99.10%, Pp of 99.77%, DER of 1.13% and ACC of 98.88% for MIT-BIH Polysomnographic database and an average Se of 99.39 %, Pp of 99.67 %, DER of 0.94 %, and ACC of 99.06% for the real-time PPG signals. © 2016 IEEE.

IEEE Region 10 Annual International Conference, Proceedings/TENCON

Effective systolic peak detection algorithm using variational mode decomposition and center of gravity

A 77 GHz polarization reconfigurable patch antenna employing radio frequency micro-electromechanical system (RF MEMS) based single-pole-single-throw (SPST) switch is designed and fabricated on 635 μm alumina substrate using surface micromachining process. The total area of the device is 0.794 mm2. Polarization diversity among linear polarization (LP), right hand circular polarization (RHCP) and left hand circular polarization (LHCP) can be produced by controlling the bias voltage of individual SPST switch. Experimental results of SPST switch and proposed antenna is presented. © 2016 IEEE.

LAMC 2016 - IEEE MTT-S Latin America Microwave Conference

77 GHz polarization reconfigurable micromachined antenna for automotive radar applications

This work presents a micro-electro-mechanical system (MEMS) based on a wide-band 4-bit phase shifter using two back-to-back single-pole-four-throw (SP4T) switches and four different distributed MEMS transmission line (DMTL) structures that are implemented on 635 μm alumina substrate using surface micromachining process. An SP4T switch is designed with a series-shunt configuration and it demonstrates an average return loss of &gt;17 dB, an insertion loss of &lt;1.97 dB and maximum isolation of &gt;28 dB up to 60 GHz. A maximum area of the SP4T switch is ∼0.76 mm2. Single-pole-single-throw and SP4T switches are capable of handling 1 W of radio frequency (RF) power up to &gt;100 million cycles at 25° C; they can even sustained up to &gt;70 million cycles with 1 W at 85 °C. The proposed wide-band phase shifter works at 17 GHz (Ku-band), 25 GHz (K-band), 35 GHz (Ka-band) and 60 GHz (V-band) frequencies. Finally,a 4-bit phase shifter demonstrates an average insertion loss of &lt;6 dB, return loss of &gt;10 dB and maximum phase error of ∼3.8° at 60 GHz frequency over 500 MHz bandwidth. Total area of the fabricated device is ∼11 mm2. In addition, the proposed device works well up to &gt;107 cycles with 1 W of RF power. To the best of the author's knowledge, this is the best reported wide-band MEMS 4-bit phase shifter in the literature that works with a constant resolution. © 2017 IOP Publishing Ltd.

Journal of Micromechanics and Microengineering

Ku to V-band 4-bit MEMS phase shifter bank using high isolation SP4T switches and DMTL structures

This paper presents a framework based on the logarithmic number system to implement adaptive filters with error nonlinearities in hardware. The framework is demonstrated through pipelined implementations of two recently proposed adaptive filtering algorithms based on logarithmic cost, namely, least mean logarithmic square (LMLS) and least logarithmic absolute difference (LLAD). To the best of our knowledge, the proposed architectures are the first attempts to implement both LMLS and LLAD algorithms in hardware. We derive error computing algorithms to realize the nonlinear error functions for LMLS and LLAD and map them onto hardware. We also propose a novel variable-α scheme to enhance the original LMLS algorithm and prove its robustness and suitability for VLSI implementations in practical applications. Detailed bit width and error analysis are carried out for the proposed VLSI fixed point implementations. Postlayout implementation results show that with an additional multiplier over conventional least mean square (LMS), 7-dB improvement in steady-state mean square deviation performance can be achieved and with the proposed variable-α scheme, 12-dB improvement can be achieved without compromising the convergence. We will show that LMLS can potentially replace LMS in practical applications, by demonstrating a proof-of-concept by extending the framework to transform domain adaptive filters. © 2017 IEEE.