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

Smart wearable and portable healthcare devices are used for continuous patient health monitoring but have limited battery power and on-board memory. Therefore, there is a huge demand for an automated photoplethysmogram (PPG) signal quality assessment (SQA) for energy-constrained smart medical devices. In this article, we propose a novel convolutional neural network (CNN)-based compressive sensing PPG-SQA method for low-power wireless healthcare devices. The main focus of this paper is to find the best compressive sensing matrix to directly classify the compressed PPG segments into noise-free and noisy segments using the optimal one-dimensional CNN architecture. The proposed CS-PPG SQA method was evaluated using 4-layer and 32 filters with a rectified linear unit (ReLU) activation function. Evaluation results showed that the deterministic binary block diagonal (DBBD) sensing matrix with a compression factor of 2 outperforms the existing methods with original PPG signal. The proposed method had an accuracy (ACC) of 99.55% for noise-free PPG (NF-PPG) versus wrist-cup noisy PPG database (MA-DB01), 99.99% for NF versus random noise-added PPG (RN-PPG) segments and 72.71% for NF-PPG versus acceleration corrupted PPG database (MA-DB02). The proposed method can reduce false alarms by discarding noisy PPG segments and reduce model size, and computational time by 50% as compared to other existing SQA methods. © 2023 IEEE.

World University root node

Department of Electrical Engineering

15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2023 - Proceedings

Compressive Sensing-Based Automatic PPG Signal Quality Assessment Using CNN for Energy-Constrained Medical Devices

In this work, an accurate estimation of the amplitudes and frequencies of the flicker components has been proposed using dynamic mode decomposition (DMD). The proposed method consists of Hilbert transform for envelope extraction, DMD for spectral analysis, and finally, the assessment of flicker severity index ( V10) based on the eye-brain model. The DMD method constructs a shifted-stack matrix of the voltage signal, and thereby it is processed through singular value decomposition (SVD) for extracting the dominant signal parameters. To verify the effectiveness of the proposed technique, voltage flickers are considered with single flicker and multiple flicker components. It is also investigated in presence of sensitive conditions such as harmonic disturbance, power frequency deviation, noisy environment, phase jumps, and transients. The simulation results are compared with the existing techniques in terms of the relative errors of flicker parameters. Finally, a hardware prototype is developed for practical flicker measurement and the proposed algorithm has been implemented on the Raspberry pi board. The experimental results are presented and compared with simulation findings of the proposed technique to prove its applicability. © 1963-2012 IEEE.

IEEE Transactions on Instrumentation and Measurement

Dynamic Mode Decomposition Based Approach for Accurate Measurement of Voltage Flicker Components

Although many pulse oximeters are available in market with different blood oxygen saturation (SpO2) equations presented in the past studies, we investigate the performance of ten existing SpO2 equations by validating the robustness of calibrated coefficients and ratio of ratios (R) parameter. For this performance study, we used the pulse transit time (PTT-PPG) database having different dual-wavelength PPG signals with their ground truth SpO2 values. From the evaluation results in terms of mean absolute error (MAE), standard deviation (SD), root mean square error (RMSE) and Bland-Altman ratio (BAR), identifying the best SpO2 equation(s) is challenging because the above-mentioned error metrics are biased with averaging of error values with large number. Therefore, this study presents a new estimation error range (EER) metric to highlight the large error values of the SpO2 estimation algorithms. On the EER metric, the best SpO2 equation is presented based on the estimation results obtained for two different durations of signal. © 2023 IEEE.

ICSIMA 2023 - 9th IEEE International Conference on Smart Instrumentation, Measurement and Applications

Performance Evaluation of Ten SpO2 Measurement Equations Using Estimation Error Range Metric and Different Signal Lengths

Energy efficiency has become the new performance criterion in this era of pervasive embedded computing; thus, accelerator-rich multi-processor system-on-chips are commonly used in embedded computing hardware. Once computationally intensive machine learning applications gained much traction, they are now deployed in many application domains due to abundant and cheaply available computational capacity. In addition, there is a growing trend toward developing hardware accelerators for machine learning applications for embedded edge devices where performance and energy efficiency are critical. Although these hardware accelerators frequently use floating-point operations for accuracy, reduced-width floating-point formats are also used to reduce hardware complexity; thus, power consumption while maintaining accuracy. Vectorization concepts can also be used to improve performance, energy efficiency, and memory bandwidth. We propose the design of a vectorized floating-point adder/subtractor that supports arbitrary length floating-point formats with varying exponent and mantissa widths in this article. In comparison to existing designs in the literature, the proposed design is 2.57x area- and 1.56x power-efficient, and it supports true vectorization with no restrictions on exponent and mantissa widths.

ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS

Design of Synthesis-time Vectorized Arithmetic Hardware for Tapered Floating-point Addition and Subtraction

In this work, a library of functionally and structurally tuneable macromolecules were synthesised and their interaction with serum proteins were studied experimentally and theoretically, with an aim to identify the driving factors that influence protein-drug interaction. The parameters taken for assessment were (a) the number and type of functional groups (dithiocarbamate, ester and amide) present in the backbone and sidechain, (b) different architectures including linear and branched, and (c) hydrophobic/hydrophilic character. Dansyl moiety was attached to make these macromolecules a suitable acceptor for FRET with serum proteins. The binding constant values calculated from the fluorescence titrations indicated that an optimum hydrophobic character, chain length and architecture are essential for a favourable interaction with BSA/HSA. By changing the above mentioned three factors, we were able to achieve a wide range of binding constant values in the range of (2.99–7.91) × 104 M−1 for BSA and (1.64–11.7) × 104 M−1 for HSA. The number of binding sites varied from 1.13 to 1.69 for BSA and 0.89 to 1.35 for HSA. Also, the energy transfer efficiency was calculated and it falls in the range of 63.9–88.7% and 52.0–84.2% respectively for BSA and HSA. The quenching mechanism for serum protein-drug complex was found to involve both static and dynamic quenching. Moreover, this structure–activity relationship was also backed by molecular docking studies. © 2023 Elsevier B.V.

Department of Chemistry

Journal of Photochemistry and Photobiology A: Chemistry

Probing the interactions of dansyl appended sequence-defined oligomers with serum albumins: Effect of functionality, hydrophobicity, and architecture

Aluminium matrix composites (AMCs) have found widespread applications in automobile, aerospace and marine industries because of their attractive properties. These include good strength to weight ratio, better corrosion and fatigue resistance. Stir casting has been identified as the most suitable method to fabricate AMCs because it provides good matrix-reinforcement bonding while also being a cost-effective process for mass production. However, this process suffers from drawbacks that include cast porosity, less wettability and agglomeration of reinforcement particles. A means to overcome these disadvantages is the use of acoustic cavitation using ultrasonic vibrations which can scatter the agglomerated particles and creates a uniform distribution of particles through acoustic streaming. Since the effect of ultrasonic cavitation is extremely localized, it is often combined with stir casting method to leverage the advantages of both approaches while negating their respective demerits. This hybrid approach known as Ultrasonic Assisted Stir Casting can be used to fabricate AMCs with better mechanical properties. The regions of cavitation as well as acoustic streaming in this process are strongly influenced by parameters such as power, wave frequency and container geometry which directly impact the performance characteristics of the process. In this work, we numerically investigate the influence of acoustic parameters and geometry of the processor container on cavitation regions and acoustic streaming. Simulation results showed that the cavitation region is concentrated near to the sonotrode and the acoustic streaming results in a circulating flow within the processor container.The analysis of cavitation zones and streaming patterns in these preliminary studies without reinforcement particles is expected to provide insights to optimize the acoustic parameters for uniform distribution of particles. © 2023 Author(s).

Department of Mechanical Engineering

AIP Conference Proceedings

Numerical investigation of ultrasonic cavitation in stir cast aluminium matrix melt composites

In SDN-based networks, contextual understanding of network behaviour and safe execution of operational decisions are important as part of the network management process. Digital Twin Network (DTN) is a promising concept which creates a virtual twin of a live SDN-based network to monitor the network from different angles with various granularities and enables it to verify an operational change without disturbing the live one. However, modelling the arbitrary decisions by an SDN Controller to form a DTN and managing the contextual information in a DTN are challenging tasks in various aspects. This paper proposes a data representation based DTN architecture integrating Knowledge Graph (KG) for data modelling and storage and Template as the context description approach. The combination of KG and Template can make the DTN management scalable with the flexibility of defining the contextual information with the relationships between network entities. It also enables efficient querying of the data storage and provides the reusability of functions and data storage for the DTN applications development. The proposed DTN architecture was implemented using an ONOS-based SDN Controller and Neo4j-based KG with built-in DTN applications for practical use. The PoC implementation exhibited short query response time and high query throughput in reading from and updating a KG, though the initial creation of a KG incurs a considerable delay which increases with its size. © 2013 IEEE.

Department of Computer Science and Engineering

IEEE Access

Building a Digital Twin Network of SDN Using Knowledge Graphs

Selective laser melting (SLM) is a widely used additive manufacturing technique in which parts are made by melting a material layer by layer. SLM can be used to make complex parts with good mechanical properties. However, a large temperature gradient due to localized heating and cooling of the powder results in residual stress and deformation. Scan strategy is an important parameter that can reduce residual stress as it is directly related to the formation of temperature gradient during the process. Scan vector length, scan rotation angle, scan pattern, scan vector orientation and hatch distance are the major parameters involved in scanning. In this study, a 3D thermo-mechanical coupled numerical model is created to analyze the effect of scanning strategy on residual stress of SLM 316L steel parts. Thermal characteristics of the SLM process are modeled by considering a moving Gaussian heat source and advection-diffusion transport equations are solved along with phase change and mass conservation to obtain the temporal evolution and spatial distribution of temperatures in the manufactured component during the whole process. The obtained temperature history is fed into the mechanical model to estimate the distribution of residual stress over the entire domain. The mentioned methodology is used to perform preliminary numerical simulations for a process involving the fabrication of a component with eight layers by using a stripe scanning strategy and 30° scan rotation angle. The modeling results show that the residual stress in the Z (out of plane) direction is less than that in the other two directions. Also the residual heat which exists in the previous tracks and layers lead to the formation of larger melt pool in the subsequent layers, and the parts with the highest temperature have the minimum equivalent stress. Equivalent stress profile also shows that compressive stresses are formed at the edges and four corners of the part while tensile stress is formed at the center of the part. © 2023 Author(s).

Effect of laser scanning strategy on residual stress in selective laser melting

A straightforward and disposable electrochemical sensor was developed by the direct electrodeposition of silver nanoparticles (AgNPs) on a conducting polymer and an acid-functionalized carbon nanotube-modified screen-printed carbon electrode (f-CNT/SPCE) for the detection of the antiviral drug valganciclovir (VGC). A scaffold layer of the conducting polymer 2,6-diaminopyridine (p-DAP) was electrodeposited on an f-CNT/SPCE surface via a potentiodynamic polarization method. Later, homogeneous deposition of AgNPs was carried out on the polymer-modified scaffold layers. The electrode activities related to the concentration of the polymer substrate and the AgNP precursor were systematically optimized. The electrochemical oxidation of VGC by SPCE/f-CNT/p-DAP-AgNPs was investigated by square-wave voltammetry. The modified sensor exhibited an attractive electroanalytical performance toward VGC with high sensitivity (nanomolar range), selectivity (in the presence of uric acid and dopamine), reproducibility, and long-term storage stability. Potential real-world applications of the modified screen-printed sensor were demonstrated using artificial urine/serum and industrial water samples. The straightforward design and attractive analytical performance of the conductive polymer-embedded AgNP -modified screen-printed sensor suggest considerable promise for use of this sensor in the point-of-care screening of antiviral drugs. The sensor strip would also be useful in the screening of antiviral drugs in industrial (quality control) and environmental (industry effluents) applications. © 2023 American Chemical Society.

ACS Applied Nano Materials

Electrochemical Strip Sensor Based on a Silver Nanoparticle-Embedded Conducting Polymer for Sensitive In Vitro Detection of an Antiviral Drug

Laser shock peening (LSP) is a novel non-contact surface modification technique to enhance the corrosion and compression behavior of stainless steel 308L (SS308L) material for a cryogenic application. SS308L is commonly used as a weld filler, weld electrode and cryogenic environment due to its low carbon content and good mechanical and high corrosion resistance properties. In this present study, wire-arc additive manufacturing (WAAM) has been utilized to manufactured SS308L material and studied the compression, corrosion, wettability and surface properties of the material. Further, the nanosecond laser shock peening has been utilized to modify the additive-manufactured SS308L surface and mechanical properties. From the experimental results, it has been observed that the uni-directional flow path (UDFP) deposition has better corrosion resistance wettability and surface finish than the bi-directional flow path (BDFP) deposition on wire-arc additive-manufactured SS308L material due to its more uniform microstructure and smoother surface finish. In addition, the post-processing treatment (LSP) of SS308L materials has improved the compressive strength of additive-manufactured SS308L material with 20.6 and 15.9% as compared to UDFP and BDFP due to grain surface refinement and increases in dislocation density. The corrosion rate of LSP-treated materials was reduced by 20.7% compared to the UDFP materials due to the higher corrosion potential and lower corrosion current density. The higher surface roughness has produced better passive film formation to enhanced corrosion resistance in the UDFP with LSP. Finally, the microstructure and chemical composition have been analyzed using scanning electron microscope and energy-dispersive x-ray spectroscopy. Graphical Abstract: [Figure not available: see fulltext.]. © 2023, ASM International.