Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram

In this paper, a Jaya-optimized feed forward neural network (FFNN) is used to model the brushless DC (BLDC) motors in the quadcopter. Precise modeling of motor-propeller performance parameters for a given throttle input, including speed, thrust, current, torque, and power is necessary for estimating energy consumption and optimizing energy sources for various flying conditions. Accurate modeling is essential for performance analysis, but it is challenging due to the nonlinear relationships, environmental factors, and variability in propeller and motor characteristics. However, by integrating experimental data with simulation and analytical models, the prediction accuracy can be enhanced. While FFNNs are effective in capturing the complex nonlinear relationships between input and output variables, their performance is highly dependent on the optimization of network weights and biases. Traditional optimization methods often face challenges such as overfitting, computational inefficiency, and sensitivity to initial conditions. By leveraging the Jaya algorithm, which requires minimal control parameters and promotes adaptive convergence, the FFNN’s predictive accuracy is significantly improved. In this research, the performance of the model is studied and verified experimentally with two different throttle conditions, namely 1). linearly increasing and 2) full flight from takeoff to landing. The following performance parameters speed, thrust, current, torque, and power are predicted with an accuracy of 96.4%, 97.5%, 97.2%, 94.9%, and 96.8%, respectively, at maximum throttle positions with respect to traditional models.

Detection of early mild cognitive impairment (EMCI) is clinically challenging as it involves subtle alterations in multiple brain sub-anatomic regions. Among different brain regions, the corpus callosum and lateral ventricles are primarily affected due to EMCI. In this study, an improved deep canonical correlation analysis (CCA) based framework is proposed to fuse magnetic resonance (MR) image features from lateral ventricular and corpus callosal structures for the detection of EMCI condition. For this, obtained structural MR images of healthy controls and EMCI subjects are preprocessed. Lateral ventricles and corpus callosum structures are segmented from these images and features are extracted. Extracted features from different brain structures are fused using non-linear orthogonal iteration-based deep CCA. Fused features are employed to differentiate healthy controls and EMCI condition using extreme learning machine classifier. Results indicate that fused callosal and ventricular features are able to detect EMCI. Improved deep CCA algorithm with tuned hyperparameters achieves the highest classifier performance with an F-score of 82.15%. The proposed framework is compared with state-of-the-art CCA approaches, and the results demonstrate its improved performance in EMCI detection. This highlights the potential of the proposed framework in the automated diagnosis of preclinical MCI conditions.

In microchannel-based cooling devices, the response time exhibits distinctive variations owing to the heterogeneous integration of the heat source and the heat sink. These variations accompanied by flow maldistribution attributes to local temperature gradients are often referred to as flow-induced high-temperature zones and develop an uneven temperature distribution in microchannel heat sinks. To explore this phenomenon, we have designed an experimental setup featuring an in-house rectangular microchannel with an integrated heat spreader. In this study, we use a nanofluid comprised of graphene oxide (GO) and water as the working fluid, aiming to understand the thermo-hydrodynamics of the heat sink for various channel aspect ratios. The experimental results show that the heat wave propagation in the heat spreader is highly directional and influenced by the nanofluids flow rate and thermal conductivity. The study demonstrated that bulk fluid diffusion of GO nanofluid increased the temperature of the heat spreader by 30%. In the case of working fluid temperature, it increased by 35% for water and 52% for GO-0.12%.

Distortions in laparoscopic videos affect surgeon visibility and surgical precision, underscoring the need for sustained high video quality. This study presents a real-time laparoscopic video quality assessment algorithm independent of reference content availability. Statistical parameters derived from luminance, local binary pattern and motion-vector maps of video frames are observed to effectively discern distortion types and severities. These parameters are used to train an evolutionary adaptive neuro-fuzzy inference system (ANFIS) end-to-end with subjective score labels. Training and validation loss curves saturate at the 85th epoch, demonstrating the model’s efficient data fitting capability. Performance comparison with other state-of-the-art methods reveals superior results, with high correlation scores of 0.9989 and 0.9446 for experts and 0.9956 and 0.9847 for non-experts, alongside low root mean square errors of 0.0828 and 0.1685 for expert and non-experts, respectively. The model accurately replicates the expert and non-expert perceptual opinions, encouraging future research in stereoscopic, augmented, and virtual reality data.

Ultrasound (US) imaging is often employed for monitoring fetal development throughout pregnancy. However, the manual detection of fetal anatomy presents several challenges to clinicians and healthcare professionals, including the structural similarity of fetal anatomical features, the position of the fetus, and the expertise of the sonographer. Artificial intelligence (AI) is now playing a significant role in developing AI-assisted tools in medical imaging to help healthcare providers and can aid in addressing challenges associated with fetal anatomy detection. Therefore, this paper proposes a spatial attention (SA) deployed convolutional neural network (CNN) called VGGSA for efficient multi-class classification of the generally used fetal biometry planes during routine examinations. A pre-trained VGG-19 CNN model is utilized as a deep feature extractor in VGGSA. The proposed VGGSA network integrates an SA module before the final pooling layer to enhance the feature representation capability of the backbone feature extractor. Leveraging the attention module into CNN helps to reduce misinterpretations caused by the inherent anatomical structural similarity between standard and non-standard fetal organs. The attention module enables the model to focus on significant regions of the images, resulting in improved classification performance. The experiments utilized two publicly available fetal US datasets to evaluate the efficacy of the proposed VGGSA network. Experimental results demonstrate that the proposed work surpasses the state-of-the-art deep learning models. The Grad-CAM technique is also applied to visualize the predictive nature of the VGGSA network.

Self-instructional media in education has the potential to address educational challenges such as accessibility, flexible and personalised learning, real-time assessment and resource efficiency. The objectives of this study are to (1) develop programmed instructions to teach design thinking concepts and (2) investigate its effects on secondary school students’ understanding of these concepts. A design thinking workshop was conducted with secondary school students; subsequently, their understanding of design thinking concepts gained through digital programmed instructions was evaluated. The study involved 33 novice secondary school students from grades 6 to 9 in India, who worked in teams to find and solve real-life, open-ended, complex problems during the workshop using the design thinking process. Data on (i) the individual performance in understanding design thinking concepts and (ii) team performance in design problem finding and solving were collected using individual tests and teams’ outcome evaluations, respectively. Students’ perceptions of the effectiveness of the programmed instructions for supporting understanding of the concepts were also captured. Results show the positive effects on students’ understanding of design thinking concepts as well as on their problem-finding and solving skills. The results justify the use of programmed instructions in secondary school curricula to advance design thinking concepts. The current version of programmed instruction has limitations, including the absence of branching mechanisms, a detailed feedback system, multimodal content and backend functionalities. Future work will aim to address these issues and overcome these shortcomings.

A patch-added planar log-periodic ring resonator meander dipole array (PPLPMDA) antenna with dimensions $\mathbf {27 \times 27 \times 0.16\ }\mathbf {\mathrm {cm}}^{\mathbf {3}}$ is developed to detect an intentional electromagnetic interference (IEMI) signal in the frequency range of 0.5–2 GHz. A meander line concept has been utilized in the radiator section to lower the antenna size. The lateral dimension of the PPLPMDA antenna is smaller by 36.65% and the horizontal dimension is smaller by 10% extra space here? compared to the conventional planar log-periodic dipole array antenna. The patches have been included for certain dipoles to get a high and nearly stable gain in the entire operating frequency range. The fabricated PPLPMDA antenna is tested in terms of S-parameters, far-field radiation performance, IEMI sensing, etc., and witnessed good agreement between simulation and measurement results. The measurements show that the PPLPMDA antenna has an average gain of $\mathbf {4.25 \pm 1.25}\mathrm {\ }\mathbf {\mathrm {dB}}$ , a reflection coefficient $| \mathbf {S}_{\mathbf {11}} |\mathbf {\leq } \mathbf {10}\mathrm {\ }\mathbf {\mathrm {dB}}$ , and a stable radiation pattern. Compared to the existing designs, the proposed PPLPMDA antenna has a compact nature without sacrificing the far-field performance, which makes it more suitable for system-level integration as a nonpowered passive sensor to detect IEMI signals.

The redox active organometallic compounds are the prominent supercapacitor electrode materials due to the attractive properties like stable redox scheme, good electron transfer mediator, and low oxidation potential. The key advantage of the organometallic compounds is that the electronic property of the electrode can be modified by altering and functionalizing the ligands around the metal atom. In this work, we synthesized cobalt‐oxyquinolinate (Co‐Q) organometallic compound by solvothermal method. To improve the electrical conductivity, the reduced graphene oxide (RGO) is grafted on Co‐Q. The electrochemical behaviour of Co‐Q is improved by the addition of RGO. The specific capacitance of Co‐Q layered on RGO is greater (1206 mF cm⁻²) than Co‐Q (670 mF cm⁻²) at current density of 1 mA cm⁻². This is due to the increase in active sites after the incorporation of RGO on Co‐Q; further, this promotes the diffusion process. The fundamental electrochemical analyses, including cyclic voltammetry, galvanostatic charge–discharge analysis, and electrochemical impedance analysis, provided the information about the electrochemical energy storage performance. Dunn's method is used to understand the diffusion and capacitive contributions of the electrode material during energy storage. From the electrochemical analysis, Co‐Q layered on RGO is a suitable material for the energy storage application due to its high diffusion and stability.

This study focused on the measurement and improvement of the radial tensile strength of composite rotors for flywheel energy storage. Filament wound carbon/epoxy composite rotors were fabricated and the radial tensile strength was measured using uniaxial tensile testing of coupons extracted from a thick rotor and diametric tensile testing of C‐rings. Compared to the uniaxial tensile test, the C‐ring test resulted in a reasonably accurate measurement of the radial tensile strength due to the absence of the effect of the curved fibers. Numerical analyses of C‐ring tests were also performed. The results matched well with the experiments and confirmed the failure of C‐rings purely because of the radial stress. The incorporation of multi‐walled carbon nanotubes in the composite rotor resulted in cohesive failure of fiber and matrix and increased the radial tensile strength by a maximum of 50%. The findings provided unique insights for accurate measurement and improvement of the radial tensile strength of composite rotors. Highlights Composite rotors were filament wound and radial tensile strength was measured. Radial tensile strength of C‐ring was accurate than that of tensile coupon. FEA of the C‐ring test validated the experimental radial tensile strength. Incorporation of MWCNT increased the radial tensile strength by 50%. Fractography showed cohesive failure indicating strong reinforcement by MWCNT.

Time Sensitive Ethernet is quickly emerging to be the preferred choice as the backbone network for in-vehicle communication, due to its high bandwidth, reliability, scalability, backward compatibility, and support for diverse traffic types. However, individual real-time control subsystems which may need to communicate via this backbone network, may be driven by non-Ethernet protocols like CAN, FlexRay, etc. Beyond ad hoc approaches, this highlights the need for a systematic message scheduling mechanism that enables seamless real-time message transmission over heterogeneous network domains. In this context , this work proposes a formal SMT (Satisfiability Modulo Theories) formulation for statically scheduling a set of persistent, periodic real-time messages. While the sources and destinations of these messages are in different CAN-network domains, the message transmissions must be conducted via an intermediate Time Sensitive Networking (TSN; IEEE802.1Q) backbone. Although , this formal strategy achieves high resource utilization while guaranteeing end-to-end timeliness, it is computationally exponential in nature with overheads becoming prohibitively expensive even for moderate problem sizes. Hence, we propose a lower overhead scheduling strategy called CAN-THER which can deliver efficient and quick solutions even for large problem sizes. The proposed scheduling strategies have been analyzed and compared using extensive simulation based experiments. Results reveal that CAN-THER is able to achieve performance that is up to 90% of the SMT formulation, while producing solutions at speeds that are approximately 10 4 times faster for problems having up to 12 CAN flows and 15 TSN switches.

Shaping multifunctional carbon fiber-reinforced polymer (CFRP) composites has gained significant attention in recent times. This study investigates the progress made in developing environmentally friendly techniques for shaping CFRP composites. The focus is on achieving sustainable manufacturing processes while maintaining the desirable properties of CFRP composites. Therefore, this review article examines the impact of machining, tools, monitoring, and cooling techniques on CFRP composites. Delamination factors are also considered from a materials engineering standpoint. The objective is to meet diverse application needs and enhance CFRP machining processes, highlighting the importance of sustainable practices in shaping the future of composite materials.

Nanocomposites are the combination of fibers, resin and nanofillers, and it is indeed the promising materials for many industrial applications. In the present study, the reinforcement Anogeissus latifolia (AL) gum powder of 40% is mixed with polyester (P) of 60% to produce unique hybrid polyester (ALP) resin matrix, the same is feeded with three different volume fractions of 1%, 3% and 5% Fly Ash Nano Powder (FANP) as a binding agent. These compositions applied on Abaca (AB), hemp (HE) and kenaf (KE) mats to produce the individual three layers composite fibers. The findings of dynamic mechanical analysis (DMA) shows that natural fiber materials with three different volume fractions (1%, 2% and 3%) mixing in each composition impregnated ALP resin have improved the stiffness compared to hybrid ALP resin. Damping factor (Tan δ) is observed to be least in AB/HE/KE mats for three different volume fractions nano powder feeding in each composition impregnated ALP resin as compared to hybrid ALP resin. The tensile strength, hardness and impact strength of raw AB fiber mats with 3% nano powder mixing in HR composites has reached the maximum value of 49.2 MPa, 17.4 BHN and 0.83 J. The improvements were 25%, 19% and 28% for the tensile, hardness and impact strengths, respectively. Keywords: Natural fiber mats (Abaca / Hemp / Kenaf); Dynamic Mechanical Analysis DMA; Anogeissus latifolia; Biodegradability

The conversion efficiency of a thermoelectric power generator depends on the dimensionless figure-of-merit (ZT) of the constituent thermoelectric materials, which is mainly determined by their Seebeck coefficient as well as the electrical and thermal conductivity. ZnO holds promise for thermoelectric applications, yet its use is currently limited by low electrical conductivity and high thermal conductivity. Herein, we demonstrate how thermal conductivity of ZnO can be significantly reduced by intelligently combining it with a cellulose-based Ag fabric using a one-step hydrothermal method, and how different ratios of zinc nitrate hexahydrate (ZNH) to hexamethylenetetramine (HMT) can be used to fine-tune the thermoelectric performance of the resulting composite. We show that as-prepared samples have a composite structure of Ag, Zn and O without any other impurity phases. We propose that the facet dependent crystal growth orientation, from the c-axis in (101) planes to the a-axis in (100) plane, amplify phonon scattering within the material, impeding effective heat transfer and thereby lowering overall thermal conductivity to 0.046 W/mK at room temperature for composites with a 1:1 ZNH to HMT ratio.

In this paper, an epidemic Susceptible–Vaccinated–Infected–Removed–Susceptible (SVIRS) model is presented on a weighted-undirected network with graph Laplacian diffusion. Disease-free equilibrium always exists while the existence and uniqueness of endemic equilibrium have been shown. When the basic reproduction number is below unity, the disease-free equilibrium is asymptotically globally stable. The endemic equilibrium is asymptotically globally stable if the basic reproduction number is above unity. Numerical analysis is illustrated with a road graph of the state of Minnesota. The effect of all important model parameters has been discussed.

Controlling both UV and visible emissions in ZnO quantum dots (QDs) possess a significant challenge due to the inherent introduction of defects during the growth process. We have refined the photoluminescence (PL) emission characteristics of ZnO QDs through a single-step, reagent-free femtosecond pulsed laser ablation in liquid (fs-PLAL) technique. The ratio of near band edge (NBE) to deep-level emission (DLE), which determines the shape of the QDs’ optical emission spectrum, is precisely controlled by the ablating laser pulse parameters— namely, pulse energy and temporal duration. Having established our ability to control the optical properties, we have investigated the mechanisms and physics involved in controlling optical emission. The key highlight of the work is that ablation with fs-pulse induces substantial defect states without altering the particle size, with the extent of the effect being dependent on the pulse energy and pulse duration. The spectroscopic techniques inducing Raman spectroscopy, excitation power dependent PL and transient PL study provided deep insight into the PL emission properties of these similarly sized QDs. The improved DLE in these laser-ablated QDs is explained by a surface-recombination-layer approximation process employing steady-state and transient PL. Moreover, we have demonstrated the applicability of green emission for pH sensing within a linear range of 7-10 and highlight the inherent antibacterial properties of these QDs.