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The performance of x-ray free electron lasers and ultrafast electron diffraction experiments is largely dependent on the brightness of electron sources from photoinjectors. The maximum brightness from photoinjectors at a particular accelerating gradient is limited by the mean transverse energy (MTE) of electrons emitted from photocathodes. For high quantum efficiency (QE) cathodes like alkali-antimonide thin films, which are essential to mitigate the effects of non-linear photoemission on MTE, the smallest possible MTE and, hence, the highest possible brightness are limited by the nanoscale surface roughness and chemical inhomogeneity. In this work, we show that high QE Cs3Sb films grown on lattice-matched strontium titanate (STO) substrates have a factor of 4 smoother, chemically uniform surfaces compared to those traditionally grown on disordered Si surfaces. We perform simulations to calculate roughness induced MTE based on measured topographical and surface-potential variations on the Cs3Sb films grown on STO and show that these variations are small enough to have no consequential impact on the MTE and, hence, the brightness.

Linear-accelerator-based applications like x-ray free electron lasers, ultrafast electron diffraction, electron beam cooling, and energy recovery linacs use photoemission-based cathodes in photoinjectors for electron sources. Most of these photocathodes are typically grown as polycrystalline materials with disordered surfaces. In order to understand the mechanism of photoemission from such cathodes and completely exploit their photoemissive properties, it is important to develop a photoemission formalism that properly describes the subtleties of these cathodes. The Dowell–Schmerge (D–S) model often used to describe the properties of such cathodes gives the correct trends for photoemission properties like the quantum efficiency (QE) and the mean transverse energy (MTE) for metals; however, it is based on several unphysical assumptions. In the present work, we use Spicer’s three-step photoemission formalism to develop a photoemission model that results in the same trends for QE and MTE as the D–S model without the need for any unphysical assumptions and is applicable to defective thin-film semiconductor cathodes along with metal cathodes. As an example, we apply our model to Cs[Formula: see text]Sb thin films and show that their near-threshold QE and MTE performance is largely explained by the exponentially decaying defect density of states near the valence band maximum.

Visual odometry (VO) is one of the promising techniques that estimates pose using the camera and does not necessarily require other sensor aiding. With increasing automation and the use of miniaturized systems such as mobile devices, wearable gadgets, & gaming consoles, demand for efficient algorithms have risen. In this paper, an attempt is made to remove the redundant features from the VO pipeline that do not have a significant effect on the estimation process. A probabilistic approach based on fast mutual information (MI) computation is suggested here as the basis for removing features. The MI value acts as a beacon for selecting distinct features while eliminating the redundant ones, thus improving the overall system speed and reducing storage requirements. The proposed MI-based feature selection framework for VO has been experimented on the KITTI vision benchmark suite and EuRoC MAV datasets available publicly. The estimated trajectory results have shown that the proposed technique is better in terms of computational efficiency and has similar accuracy as compared to the normal VO pipeline. Further investigations have also been carried out over the VSLAM framework to test its applicability in a real-time system.

Growth of organic thin films using physical vapor deposition typically follows a three-dimensional mode, resulting in a rough surface, which undermines their application potential. To address this issue, we have studied the effect of electric field and temperature on the growth dynamics, especially the heterogeneous nucleation process, of croconic acid (CA) films, taking advantage of the large dipole moment of the molecules and the ferroelectric polarization of the molecular crystals. We found that the nucleation rate reaches the maximum at intermediate temperature, and the electric field shifts the maximum nucleation rate towards lower temperature. An analysis using the classical nucleation theory suggests that the electric field decreases the sublimation temperature, increases the wetting angle, and decreases the surface diffusion barrier. These results provide important insight into the growth of molecular crystal films under electric fields and pave the way to fabricating films with better surface characteristics for molecular ferroelectric films.

Copyright: © 2021 by the authors. Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates. The complementary filter is one of the widely adopted techniques whose performance is highly dependent on the appropriate selection of its gain parameters. This paper presents a novel cascaded architecture of the complementary filter that employs a nonlinear and linear version of the complementary filter within one framework. The nonlinear version is used to correct the gyroscope bias, while the linear version estimates the attitude angle. The significant advantage of the proposed architecture is its independence of the filter parameters, thereby avoiding tuning the filter’s gain parameters. The proposed architecture does not require any mathematical modeling of the system and is computationally inexpensive. The proposed methodology is applied to the real-world datasets, and the estimation results were found to be promising compared to the other state-of-the-art algorithms.

Magnetocrystalline anisotropy (MCA) is one of the key parameters investigated in spin-based electronics (spintronics), e.g. for memory applications. Here, we employ first-principles calculations to study MCA in thin film full Heusler alloy Co

Vision-based localization is one of the major aspects of industrial and space robotics. Though many sensing modalities exist for motion estimation, cameras have been used widely due to its availability and reduced cost. Visual odometry estimates the motion parameters of a camera through the images it captures. Multiple sensing modalities are fused to improve estimation accuracy with increased cost. With the success of deep learning architectures in the area of computer vision, one of the recent paradigm shift occurred in visual odometry is estimating motion using non-geometric schemes by the end-to-end manner. The different stages of the traditional visual odometry pipeline are estimated as a single function mapping input images to output 6 DoF pose of the camera. There are many ways to apply deep learning in visual odometry, one of the common techniques is through transfer learning. In this work, analysis has been done on traditional DeepVO and ResNetVO by incorporating a novel architecture splitting and independent learning scheme. The estimation results show the efficacy of the proposed algorithm.

Lens distortion estimation and correction has been an active research area for decades and continues to intrigue scientists even today. With increasing usage of low-cost wide-angle lenses for commercial applications, this demand has increased even more as these lenses have relatively larger distortions. It is thus required to devise mechanisms by which these errors can be removed automatically and in an effective manner. In this paper, a neural network based technique is devised to estimate radial distortion by analysing the area enclosed by synthesizing objects of known shape. The difference in the area between ideal and distorted image is used to create a model and used later to assess its efficacy on the test dataset. The proposed scheme is found to work promisingly on synthetic images and can be extended for real-time image analysis and correction in future.

Real-time image processing in the field of computer vision is not only a challenging task, but requires several embedded computing considerations. In the present era, FPGA-based reconfigurable architecture is widely used for its parallel computing compatibility and fast hardware prototyping. In this paper, a 2D convolution operation is applied to the complete image by splitting it into vertical blocks on which row buffering is applied separately without any substantial increase in the buffer storage area. The proposed technique reduces the execution time with each parallelism stage and utilizes lesser hardware resource as compared to other partitioning schemes. As compared to several existing parallelizing scheme for data processing through FPGA, vertical splitting has been found to work promisingly. Additionally, a table indicating the optimum parallelism stages achievable with given timing constraints, memory bandwidth and, available resources is provided.

With the increase in the need for video-based navigation, the estimation of 3D coordinates of a point in space, using images, is one of the most challenging tasks in the field of computer vision. In this work, we propose a novel approach to formulate the triangulation problem using Sampson’s distance, and have shown that the approach theoretically converges toward an existing state-of-the-art algorithm. The theoretical formulation required for achieving optimal solution is presented along with its comparison with the existing algorithm. Based on the presented solution, it has been shown that the proposed approach converges closely to Kanatani–Sugaya–Niitsuma algorithm. The purpose of this research is to open a new frontier to view the problem in a novel way and further work on this approach may lead to some new findings to the triangulation problem.

Attitude estimation is one of the core fundamentals for navigation of unmanned vehicles and other robotic systems. With the advent of low cost and low accuracy micro-electro-mechanical systems (MEMS) based inertial sensors, these devices are used ubiquitously for all such commercial grade systems that need motion information. However, these sensors suffer from time-varying bias and noise parameters, which need to be compensated during system state estimation. Complementary filtering is one of such techniques that is used here for estimating attitude of a moving vehicle. However, the complementary filter structure is dependent on user fed gain parameters, KP and KI and needs a mechanism by which they can be obtained automatically. In this paper, an attempt has been made towards addressing this issue by applying least square estimation technique on the error obtained between estimated and measured attitude angles. The proposed algorithm simplifies the design of nonlinear complementary filter by computing the filter gains automatically. The experimental investigation has been carried out over several datasets, confirming the advantage of obtaining gain parameters automatically for the complementary filtering structure.

Attitude estimation plays a major role in the autonomy of unmanned aerial vehicles and requires fusion of different sensor measurements. This paper describes an adaptive estimation scheme in which the weight parameter for the complementary filter (CF) is varied over time. The adaptive mechanism proposed here is inspired from the multiple model adaptive estimation (MMAE) scheme used for varying noise parameters in the Kalman filter structure. In this paper, the linear complementary filters are used as elementary blocks in the MMAE structure and their weights are modified probabilistically to obtain an accurate orientation estimate. It avoids the problem of manual selection of weight factor for complementary filter and provides a robust orientation estimate against varying system dynamics. The proposed MMAE based adaptive CF scheme is modular in nature and is dependent on the residual error between estimated and the measured orientation angle. It is applied on the real world datasets logged from inertial sensors and the performance of MMAE based CF structure is found to work promisingly as compared to the non-linear complementary filter versions and the extended Kalman filter framework.

This paper presents a novel method for attitude estimation of an object in 3D space by incremental learning of the Long-Short Term Memory (LSTM) network. Gyroscope, accelerometer, and magnetometer are few widely used sensors in attitude estimation applications. Traditionally, multi-sensor fusion methods such as the Extended Kalman Filter and Complementary Filter are employed to fuse the measurements from these sensors. However, these methods exhibit limitations in accounting for the uncertainty, unpredictability, and dynamic nature of the motion in real-world situations. In this paper, the inertial sensors data are fed to the LSTM network which are then updated incrementally to incorporate the dynamic changes in motion occurring in the run time. The robustness and efficiency of the proposed framework is demonstrated on the dataset collected from a commercially available inertial measurement unit. The proposed framework offers a significant improvement in the results compared to the traditional method, even in the case of a highly dynamic environment. The LSTM framework-based attitude estimation approach can be deployed on a standard AI-supported processing module for real-time applications.

Attitude estimation is one of the core frame- works used for navigating an unmanned aerial vehicle from one place to the other. This paper presents an Euler-based non-linear complementary filter (CF) whose gain parameters are obtained using particle swarm optimization (PSO) technique. It relieves the user from feeding the KP and KI parameters manually and adjust these parameters automatically when the error between the attitude measured from accelerometer and the CF increases above a particular threshold. The measurement unit for this research consists of micro-electro-mechanical-systems (MEMS) based low cost tri-axial rate gyros, accelerometers and magnetometers, without resorting to global positioning system (GPS) data. The efficiency of the CF is experimentally investigated with the help of reference attitude and the raw sensor data obtained from commercial inertial measurement unit (IMU). Simulation results based on the test data show that the proposed PSO aided non-linear complementary filter (PNCF) can automatically obtain the required gain parameters and exhibits promising performance for attitude estimation.

Visual odometry is a popular technique used to estimate motion in GPS-challenged environment, whose accuracy depends on the features extracted from the images. In past attempts to improved feature distinctiveness, these features have become complex and lengthier, requiring more storage space and computational power for matching. In this paper, an attempt is made toward reducing the length of these feature descriptors while maintaining a similar accuracy in pose estimation. Elimination of feature indices based on variance analysis on feature column sets is proposed and experimented in this paper. The features with reduced descriptor length are applied over the 3D-2D visual odometry pipeline and experimented on KITTI dataset for evaluating its efficacy. The proposed scheme of variance-based descriptor length reduction is found to reduce the overall time taken by the motion estimation framework while estimating the transformation with similar accuracy as that with full-length feature vector.

With rapid advancements in the area of mobile robotics and industrial automation, a growing need has arisen towards accurate navigation and localization of moving objects. Camera-based motion estimation is one such technique which is gaining huge popularity owing to its simplicity and use of limited resources in generating motion path. In this chapter, an attempt is made to introduce this topic for beginners covering different aspects of vision-based motion estimation task. The theoretical section provides a brief on different computer vision fundamentals specific to pose estimation task followed by a systematic discussion on the visual odometry (VO) schemes under different categories. The evolution of VO schemes over last few decades is discussed under two broad categories, that is, geometric and non-geometric approaches. The geometric approaches are further detailed under three different classes, that is, feature-based, appearance-based, and a hybrid of feature and appearance based schemes. The non-geometric approach is one of the recent paradigm shift from conventional pose estimation technique and is discussed in a separate section. Towards the end, a list of different datasets for visual odometry and allied research areas are provided for a ready reference.

This paper aims to develop an Adaptive Sliding Kalman Filter (ASKF) by fusing the concept of change detection in a data stream, adapting noise covariance matrices and the Sliding Kalman filter (SKF). Adaptive Kalman filtering (AKF) scheme modifies the noise covariance matrix (Q and R) value based on a window of past innovation sequence whereas SKF is a window based filtering technique which uses past information to obtain the present state estimate. However, the length of the window chosen for SKF and AKF is arbitrary and a scheme has been devised here to adapt this window length according to the data stream statistics. The change detection scheme chosen here does not make any assumption on the data distribution and is sequential in nature, such that a change is triggered whenever the underlying statistics of data crosses a pre-determined threshold. The key contribution of this work is toward the formulation of a mechanism by which the window length is made adaptive such that whenever a change is detected, the window length for SKF and AKF is curtailed and restarted in an oscillatory windowing fashion. The suggested filter is robust against temporary uncertainties and appropriate for reliable estimation of signals that may arise in many engineering areas. Real world experimental results demonstrate better estimation accuracy of the proposed method than that of others.

Kalman filter based algorithms aim at providing accurate estimate of the state parameters which is indirectly governed by the accuracy of the sensor measurement and noise parameters fed to the system model. Multiple Model Adaptive Estimation (MMAE) is one of the adaptive techniques which tries to reduce the dependency of Kalman filter on the noise parameters fed to the system. The main goal of this work is to improve state estimation by incorporating window size as one of the unknown parameters in MMAE framework, referred to as Window based MMAE (WMMAE). The proposed scheme intertwines the concepts of Innovation Adaptive Estimation (IAE) and MMAE in one structure and the state estimation for each model is implemented by IAE. Simulation results prove the efficacy of WMMAE scheme as compared to MMAE and its other variants.

Purpose – The purpose of this paper is to solve the problem of tuning of EKF parameters (process and measurement noise co-variance matrices) designed for attitude estimation using Global Positioning System (GPS) aided inertial sensors by employing a Human Opinion Dynamics (HOD)-based optimization technique and modifying the technique using maximum likelihood estimators and study its performance as compared to Particle Swarm Optimization (PSO) and manual tuning. Design/methodology/approach – A model for the determination of attitude of flight vehicles using inertial sensors and GPS measurement is designed and experiments are carried out to collect raw sensor and reference data. An HOD-based model is utilized to estimate the optimized process and measurement noise co-variance matrix. Added to it, few modifications are proposed in the HOD model by utilizing maximum likelihood estimator and finally the results obtained by the proposed schemes analysed. Findings – Analysis of the results shows that utilization of evolutionary algorithms for tuning is a significant improvement over manual tuning and both HOD and PSO-based methods are able to achieve the same level of accuracy. However, the HOD methods show better convergence and is easier to implement in terms of tuning parameters. Also, utilization of maximum likelihood estimator shows better search during initial iterations which increases the robustness of the algorithm. Originality/value – The paper is unique in its sense that it utilizes a HOD-based model to solve tuning problem of EKF for attitude estimation.

Space vehicle re-entering the earth's atmosphere is surrounded by a layer of plasma, preventing radio communication from the vehicle, leading to a phenomena called ‘radio blackout’. In this study, the concept of guiding high power laser through plasma has been discussed for establishing communication with the space vehicle during re-entry. It is found that, with increase in relativistic electron mass the refractive index of the plasma medium increases, displaying properties similar to that of converging lens. It is also shown that the power of the laser, if maintained above a critical limit, will assist in focussing of waves. The self-focusing property of plasma is therefore explored for achieving this target and the theoretical proof has been provided.

An electrically written domain structure formed by a biased tip, and visualized in the piezoresponse force microscopy mode, shows stable charged domain walls in the organic ferroelectric diisopropylammonium chloride microcrystal.

Half-metallic Heusler alloys have been intensively studied in recent years due to their potential applications in spin-based devices, e.g., in magnetic tunnel junctions. Yet, their properties may be very sensitive to the choice of the substrates, i.e., to the epitaxial strain and interface properties. Here, we report the results of our computational work on the half-Heusler compound CrMnSb(1−x)Px. In particular, we demonstrate that the parent compound CrMnSb is close to a half-metallic material at the optimized lattice parameter, with the onset of the half-metallic bandgap a few meV above the Fermi energy. Moreover, although it undergoes a half-metallic transition under a uniform compression of ∼1.5%, such a transition is absent under epitaxial strain. At the same time, we show that a half-metallic transition could be induced by a chemical substitution of Sb with P, which results in a volume reduction of the unit cell. In particular, 50% substitution of Sb with P leads to a robust half-metallicity in CrMnSb(1−x)Px, with 100% spin polarization being retained at a large range of epitaxial strain. Thus, our results indicate that CrMnSb0.5P0.5 could be grown on different types of substrates, e.g., GaAs, without its electronic properties being detrimentally affected by biaxial strain. In addition, CrMnSb0.5P0.5 exhibits a fully compensated ferrimagnetic alignment, which could be potentially useful in applications where stray magnetic fields are undesirable.

Inertial sensor calibration is one of the most important aspects of estimating motion using an inertial navigation system. The traditional calibration technique requires mounting of sensor in different orientations using costly equipment which are not available many a times for calibrating low cost MEMS sensors. The infield calibration scheme helps in obtaining the calibration parameters directly by minimizing a multi-dimensional cost function built around principles that these sensors follow. In this paper, a novel in-run calibration algorithm is proposed that does not require the sensor to be mounted in specific orientations. This scheme updates calibration parameter even without dismounting it from its location. In this work particle swarm optimization technique with scale free network is compared with other PSO variants and recommended to be used for inertial sensor calibration. The proposed in-run calibration scheme is run on simulated and real world dataset to investigate its efficacy on uncalibrated sensor readings.

Magnetoelectric layers with a strong coupling between ferroelectricity and ferromagnetism offer attractive opportunities for the design of new device architectures such as dual-channel memory and multiresponsive sensors and actuators. However, materials in which a magnetic field can switch an electric polarization are extremely rare, work most often only at very low temperatures, and/or comprise complex materials difficult to integrate. Here, we show that magnetostriction and flexoelectricity can be harnessed to strongly couple electric polarization and magnetism in a regularly nanopatterned magnetic metal/ferroelectric polymer layer, to the point that full reversal of the electric polarization can occur at room temperature by the sole application of a magnetic field. Experiments supported by finite element simulations demonstrate that magnetostriction produces large strain gradients at the base of the ferroelectric nanopillars in the magnetoelectric hybrid layer, translating by flexoelectricity into equivalent electric fields larger than the coercive field of the ferroelectric polymer. Our study shows that flexoelectricity can be advantageously used to create a very strong magnetoelectric coupling in a nanopatterned hybrid layer.

Color image modification is an essential component for several applications and the grayscale transformation is generally mapped to the color image indirectly. Although several techniques have been used for this transfer, they suffer from gamut mapping issue. In this paper, it is aimed to study the mapping of grayscale transformations to the color scale in different perspectives. Modifying the image in different color space than the original retains hue to a promising extent, but suffers from the gamut problem. A generic scheme to map grayscale changes to the color space for all kinds of spatial modification is proposed here. The hue preserving color image enhancement (HPCE) scheme discussed here is free of gamut-mapping issue and shows promising results in transferring the grayscale transformation to the color image in a simplistic manner. The proposed HPCE scheme is analysed qualitatively through visual appearance and quantitatively using color difference metrics SHAME and CID, gray image difference and EBCM measures. Different gray scale transformations such as S-type enhancement and different forms of histogram equalization techniques are applied on Berkeley dataset of 500 images to prove the efficacy of proposed algorithm. This is a post-peer-review, pre-copyedit version of an article published in [Sensing and Imaging] Locked until 17.8.2019 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/s11220-018-0219-6

Visual odometry is a widely known art in the field of computer vision used for the task of estimating rotation and translation between two consecutive time instants. The RANSAC scheme used for outlier rejection incorporates a constant threshold for selecting inliers. The selection of an optimum number of inliers dispersed over the entire image is very important for accurate pose estimation and is decided on the basis of inlier threshold. In this paper, the threshold for inlier classification is adapted with the help of fuzzy logic scheme and varies with the data dynamics. The fuzzy logic is designed with an assumption about the maximum possible camera rotation that can be observed between consequent frames. The proposed methodology has been applied on KITTI dataset, and a comparison has been laid forth between adaptive RANSAC with and without using fuzzy logic with an aim of imparting flexibility to visual odometry algorithm.