Publication2

In this paper, a 2 inverted U-slot integrated rectangular patch antenna array is proposed that can be used for S band applications i.e., radar applications, communication and wireless networks. In the proposed antenna design 2 inverted U slots are introduced in the patch to improve bandwidth providing a bandwidth of 150 MHz and gain of 4.08 dBi resonating at 3.3 GHz. Furthermore, 4 X 4 antenna array is fabricated on 1.6 mm thick FR4 substrate with dielectric constant 4.4 providing a gain of 14.2 dBi. Ansys HFSS is used for antenna simulation and a prototype is fabricated and tested.

Curve reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability as well as potential extension to 3D for surface reconstruction have led to many different algorithms. We survey the literature on 2D curve reconstruction and then present an open-sourced benchmark for the experimental study. Our unprecedented evaluation of a selected set of planar curve reconstruction algorithms aims to give an overview of both quantitative analysis and qualitative aspects for helping users to select the right algorithm for specific problems in the field. Our benchmark framework is available online to permit reproducing the results and easy integration of new algorithms.

Generation of current or potential at nanostructures using appropriate stimuli is one of the futuristic methods of energy generation. We developed an ambient soft ionization method for mass spectrometry using 2D-MoS2, termed streaming ionization, which eliminates the use of traditional energy sources needed for ion formation. The ionic dissociation-induced electrokinetic effect at the liquid-solid interface is the reason for energy generation. We report the highest figure of merit of current generation of 1.3 A/m2 by flowing protic solvents at 22 μL/min over a 1 × 1 mm2 surface coated with 2D-MoS2, which is adequate to produce continuous ionization of an array of analytes, making mass spectrometry possible. Weakly bound ion clusters and uric acid in urine have been detected. Further, the methodology was used as a self-energized breath alcohol sensor capable of detecting 3% alcohol in the breath.

Photovoltaic Thermal (PVT) systems generate hot water and electricity simultaneously. The grade of thermal energy generated by such PVT systems is low, resulting in limited application. To overcome this challenge, the coupling of the PVT system to a secondary flat plate collector (FPC) has been explored in this work. For the system's performance estimation, a 3-D numerical model has been developed for both glazed and unglazed PVT-FPC collector. Based on the numerical model, PVT collectors have been fabricated, and each is then connected in series to a commercially available FPC collector. Experiments conducted on the fabricated PVT-FPC system showed a close match between simulation and experiments. Further, experiments conducted on the unglazed PVT – FPC collector showed a peak outlet water temperature of 60–63 °C at 30 LPH. For the unglazed PVT collector, the peak electrical and thermal efficiency of 16% and 25% is reported respectively, whereas for the FPC collector, the thermal efficiency of 35% is reported. Compared to an individual unglazed PVT collector, 17 °C higher outlet water temperature is reported for the coupled system. Similarly, for the case of glazed PVT-FPC collector, the peak outlet water temperature reported is 65–67 °C at 30 LPH with 14–15 °C higher outlet water temperature.

While analytical solutions of critical (phase) transitions in dynamical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical systems. A key example is thermoacoustic instability in combustion, where prediction or early detection of the onset of instability is a hard technical challenge, which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries. The instabilities arising in combustion chambers of engines are mathematically too complex to model. To address this issue in a data-driven manner instead, we propose a novel deep learning architecture called 3D convolutional selective autoencoder (3D-CSAE) to detect the evolution of self-excited oscillations using spatiotemporal data, i.e., hi-speed videos taken from a swirl-stabilized combustor (laboratory surrogate of gas turbine engine combustor). 3D-CSAE consists of filters to learn, in a hierarchical fashion, the complex visual and dynamic features related to combustion instability from the training videos (i.e., two spatial dimensions for the image frames and the third dimension for time). We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions. We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video. The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability. The machine learning-driven results are verified with physics-based off-line measures. Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions.

Corrosion in steel reinforcement has a severe impact on the performance of the structures. It is one of the important factors for the degradation of structures. It leads to volume expansion due to corrosion products, cracking and splitting of concrete and degradation of bond between the steel reinforcement and concrete. Most of the numerical studies carried out are based on artificial corrosion but there is lack of study with respect to the 3D nonlinear finite element analysis of a naturally corroded beam. The concrete and reinforcement is modelled based on a constitutive model which is based on nonlinear fracture mechanics and von-mises plasticity yield criterion respectively. Bond and corrosion model which was previously developed is used in this analysis. Interface 2D elements are used for the surface between the concrete and steel reinforcement. The analysis results indicates that the beam analyzed shows a variation of around 20% with respect to the existing literature for a naturally corroded beam.

The limited availability of conventional energy carriers has increased the demand for renewable energy sources to cater to the ever increasing energy demand. The present chapter focusses on phosphoric acid fuel cells (PAFCs) detailing their working principles and application potential. The different materials used in PAFCs, including electrolyte, catalysts and bipolar plates and their importance in the ultimate performance are summarized. The degradation behavior due to acid leaching which affect several components of the cell is outlined. Researchers’ contributions towards early identification of degradation of catalyst and catalyst support in particular due to the presence of excess quantity of phosphoric acid, is also described. The importance of having anticorrosive coatings on bipolar plates is outlined in view of achieving life enhancement of the cell operation. Significant work has been carried out in PAFC modeling (1D–3D, steady state and dynamic models) and these are reviewed. The important design considerations during scale-up such as gasket selection and its arrangement, planarity of electrodes, fabrication method of membrane electrode assemblies (MEAs), thermal stress management etc. are described in detail. The chapter outlines various applications explored from power ratings of 20–500kW and also future requirements to be addressed in material as well as engineering issues.

Application of intelligent data analytics using machine learning in management of 5G networks can enable autonomous networking capabilities in 5G networks. This paper describes the design and implementation of CygNet MaSoN, a management system supporting advanced aggregation and analytics features combined with machine learning. The system supports detection of anomalous network behaviour, detection of degradation in network performance and service quality and also supports resource optimization. The main objective is to achieve self-organizing and closed loop automation functionalities expected as part of autonomous functioning of 5G networks. Details of the system architecture and components are presented. Three real-life use cases implemented on this system are then described. Machine learning models built and synthetic data generation methods adopted are presented with the features considered. The results obtained using the MaSoN system are also presented to demonstrate the effectiveness of the system in 5G network operations.

We present a supply supervisory circuit that makes use of an adaptively biased current comparator achieving fast response time (1.8 μ s) while consuming very low quiescent current (Iq). A Burst mode charge-pump is used to ensure monitoring down to 0.75V. This is the lowest reported common monitoring and operating supply for supervisory circuits. Fabricated in 130nm CMOS, this work achieves an Iq(nA) x Delay (μ s) FoM of 29, a 70x improvement over state-of-the-art supply supervisory circuits.

This research presents a rule-based Anti-lock Braking System (ABS) algorithm towards active vehicle safety in Heavy Commercial Road Vehicles (HCRVs). Wheel Slip Regulation (WSR) algorithms, that are constituents of an ABS, are typically either model-based or rule-based. Model-Based Algorithms (MBAs) utilise mathematical models that characterise vehicle dynamics and are intensive in their demand for real-time information. On the other hand, Rule-Based Algorithms (RBAs) operate on set rules with pre-defined thresholds and utilise data from sensors that are typically available on the vehicle. A great deal of advancement has been reported in literature related to MBAs. However, most commercially available RBAs are proprietary in nature and the finer details are seldom revealed. Hence, this work proposes an RBA, which is a combined Slip and Wheel Acceleration Threshold Algorithm (SWATA), including a framework for identifying the importance of thresholds and their magnitudes. SWATA was tested on a Hardware-in-Loop (HiL) setup across varying road and loading conditions, and it provided a maximum of 36% braking distance improvement compared to a case where it was inactive. An adaptive version–ASWATA–is also proposed that can adapt the thresholds according to the particular tyre-road interface. Additionally, a quantitative comparison of the proposed RBA with a Sliding Mode Control (SMC) based MBA, which was developed and tested on the same HiL setup, is presented. It was observed that the performance of the RBA was on par with that of the MBA for most test cases, but with minimal data requirements.

This paper proposes a transmission-line based bidirectional reflection-type phase shifter for the 2.5 to 3.2 GHz frequency band. Open and short reflective loads are used to double the available phase shift and reduce insertion loss. The transmission line is implemented using inductors and MOS switches in a 65 nm RF CMOS process. This work achieves a total phase shift of 180° with phase resolution of 22.5° at 2.85 GHz and rms error of 1.2°. The insertion loss of the phase shifter is 5.7 dB and the loss variation is less than 0.3 dB.

This work presents a 5 Gb/s PAM4 hybrid voltage-mode transmitter with current mode continuous-time linear equalization. The transmitter achieves a large output signal swing with a PAM4 CMOS output driver. CTLE is embedded in the transmitter's output stage to compensate a range of channel loss without reducing the signal swing at the receiver front end. Fabricated in 65 nm CMOS process, the transmitter dissipates 20.4 mW in the output driver at 5 Gb/s while transmitting 1.1 V peak-to-peak differential output swing across 2 m UTP cable.

Sensing the angle of bending of a soft robotic finger is valuable in many applications but it is a non-trivial task. In this paper, a simple but effective sensing approach based on variable mutual inductance is presented to sense the bending angle. Existing inductive bend sensors use concentric coil based designs and they are not easy to manufacture and integrate into the finger. The planar coil based bend sensor, proposed in this work, is integrated inside the soft robotic finger. The sensor comprises of two flexible printed circuit boards based spiral coils that are magnetically coupled. Three such units are proposed to use in a finger. Each set of planar coils is arranged in such a manner that they give a measure of the curvature of the finger. It can give localized bending of the corresponding parts of the structure which is useful if the bending is not uniform. The measurement circuit required for the sensor is very simple. A prototype sensor built and tested showed a repeatable input-output characteristic with a repeatability error of 0.6%. The proposed sensor does not use the grasping area, is easy to integrate and less expensive. The output of the sensor is immune to moisture, dust and oil.

Most of the existing flow sensors are expensive and limited in their capabilities for sensing bidirectional flow. Low-cost and accurate flow sensors with bidirectional sensing capability have numerous applications in the residential and irrigation sectors. Evaluation of a low-cost, cantilever-based sensor, suitable for measuring flow rates under turbulent flow conditions is presented in this article. Such sensors are reported for micro-fluidic applications but its potential application in large diameter pipes under turbulent flow has not been studied yet. A cantilever formed using a thin stainless-steel strip is used as the sensing element in the proposed sensor. One of the ends of the cantilever is firmly fitted to the inner wall of the pipe, and it bends or deflects towards the direction of the flow as a function of the flow rate. To experimentally evaluate the sensor in detail, the mean deflection angle of the cantilever is measured using a camera, and an image processing algorithm. In practice, the angle can be sensed using simpler methods. The performance of the prototype sensor has been evaluated after building an appropriate regression model. The results are subsequently expressed in terms of the mean flow velocity, thereby providing its potential utility in pipes of other dimensions. The shape of the mean flow velocity with respect to the mean angle of deflection characteristic of the proposed sensor matched well with the theoretical deflection computed. The sensor developed has given an accuracy of 3 % of full scale, for flow rates in the range of 2-15.5 m3/hr. The proposed sensing mechanism can realize cost-effective, simple, and reliable flow sensors. Such sensors will find applications in residential and industrial domains.

Automation of construction processes facilitates increased productivity and overall higher project performance. This paper presents a methodology for comparative assessment of different construction processes and selection of an optimal solution based on appropriate automation implementation. Construction processes are quantitatively evaluated using a methodology combining case-based reasoning and compositional modeling. Through the generation of many combinations of process fragments that are compiled from case libraries, potential solutions are explored and evaluated. An example involving solutions such as RCC frame construction, precast construction, and modular steel frame construction is described in this paper. The study demonstrates the possibility of selection of suitable construction processes based on the quantitative assessment of a large number of potential solutions. Processes are modelled by decomposing them up to the elementary tasks and appropriate level of automation is identified in all the tasks.

The aim of this work is to employ a modified cell-based smoothed finite element method (S-FEM) for topology optimization with the domain discretized with arbitrary polygons. In the present work, the linear polynomial basis function is used as the weight function instead of the constant weight function used in the standard S-FEM. This improves the accuracy and yields an optimal convergence rate. The gradients are smoothed over each smoothing domain, then used to compute the stiffness matrix.Within the proposed scheme, an optimum topology procedure is conducted over the smoothing domains. Structural materials are distributed over each smoothing domain and the filtering scheme relies on the smoothing domain. Numerical tests are carried out to pursue the performance of the proposed optimization by comparing convergence, efficiency and accuracy.

In this study, we present a displacement based polygonal finite element method for compressible and nearly-incompressible elastic solids undergoing large deformations in two dimensions. This is achieved by projecting the dilatation strain onto the linear approximation space, within the framework of volume averaged nodal projection method. To reduce the numerical integration burden over polytopes, a linear strain smoothing technique is employed to compute the terms in the bilinear/linear form. The salient features of the proposed framework are: (a) does not require derivatives of shape functions and complex numerical integration scheme to compute the bilinear and linear form and (b) volumetric locking is alleviated by adopting the volume averaged nodal projection technique. The efficacy, convergence properties and accuracy of the proposed framework is demonstrated through four standard benchmark problems.

Heat recirculating (HR) geometries have been studied in the literature for their use in energy generation applications. This work investigates the ignition and cold-start behavior of lean propane-air combustion in a U-bend catalytic microreactor through computational fluid dynamics (CFD) study. Although several studies have focused on stability and combustion behavior, the role of heat recirculation on catalytic and homogeneous ignition from the cold-start conditions has not been analyzed so far. The U-bend microreactor is a typical example of HR geometry, where the hot exhaust gases in the recirculation channel transfer heat to the cold incoming fluid. Significance of heat recirculation across the dividing wall on the ignition temperature, as well as transient evolution of temperature, propane conversion and reaction are presented. Ignition temperature is found to be 20 K lower in the U-bend microreactor than straight channel microreactor. However, transient ignition study revealed that at nominal conditions, U-bend microreactor takes about 35 s longer than straight channel for ignition and to reach steady state, owing to heat distribution internally within the U-bend microreactor. The effect of flowrate is analyzed, which shows that at higher flowrates, the ignition and steady-state times are also lower for the U-bend microreactor. Finally, the effect of solid wall material is investigated, which shows that lower conductivity material (such as ceramic) showed faster ignition with low thermal inputs.

The paper studies the high-temperature structure of a chaotic potential (CP) induced in heterojunctions of the group III nitrides by the electrostatic field of charged dislocations. The CP amplitude in the junction plane has been obtained taking into account the spatial dispersion of a dielectric response of two-dimensional electron gas. The dependence of the CP properties on the parameters of the system was found. In particular, the magnitude of the CP amplitude exceeds that of the thermal energy, if the two-dimensional non-degenerate gas given in III-nitride heterojunctions and the dislocation densities being up to and over 1010 cm–2

We present a low phase noise 4.5-to-6.5 GHz injection-locked oscillator-based frequency tripler (ILT) from an ultra-low jitter 1.5-to-2.16 GHz clock source. Class-C biasing is employed in the digitally controlled LC oscillator (LC-DCO) and the injection circuit to simultaneously achieve low phase noise in the LC-DCO and improve the third harmonic injection strength. Using BJT in the injection transistors and DCO greatly improves the low-frequency phase noise performance of the ILT. Fabricated in a 0.13 mu mathrm{m} BiCMOS process, the ILT has a measured tuning range of 4.5-to-6.5 GHz, with a jitter tracking bandwidth of 25 MHz for sub-harmonic injection. The ILT demonstrates good sub-harmonic rejection ratios SHRR1 and SHRR2 as 48 dB and 58 dB, respectively. The ILT adds 15 fs additional rms jitter to an input clock source with rms jitter of 68 fs.