Wireless Emitter Location Estimation Based on Linear and Nonlinear Algorithms using TDOA Technique

Low-power devices such as cell phones, and wireless routers are commonly used to control Improvised Explosive Devices (IEDs) and as the communication nodes for the sake of command and control. Quickly locating the source of these signals is ambitious, specifically in a metropolitan environment where buildings and towers may cause intervention. This presents a geolocation system that compounds the attributes of several proven geolocation and error mitigation methods to locate an emitter of interest in an urban environment. The proposed geolocation system uses a Time Difference of Arrival (TDOA) approach to estimate the position of the emitter of interest. Using multiple sensors at known locations, TDOA estimates are achieved by the cross-correlation of the signal received at all the sensors. A Weighted Least Squares (WLS) solution, Linear least Square (LLS) method and maximum likelihood (ML) estimation is used to estimate the emitter's location. If the variance of this location estimate is too high, a sensor is detected and identified as possessing a Non-Line of Sight (NLOS) path from the emitter. This poorly located sensor is then removed from the geolocation system and a new position estimate is computed with the remaining sensor TDOA information. The performance of the TDOA system is determined through modeling and simulations. Test results confirm the feasibility of identifying a NLOS sensor, thereby improving the geolocation system's accurateness in a metropolitan environment

Analysis of results of nonunion tibial fractures Ilizarov external fixation

Background: Tibia is most exposed bone in the body and vulnerable to trauma and therefore its fractures are common among the long bone fractures. Tibia is the common site of non-union in long bone fractures. Management of non-union in long bone fractures is a challenging problem for orthopedic surgeons. When the defect is more than 4 cm then it needs bone transport.Methods: Materials of this study comprised 20 cases of nonunions of tibia treated over 2½ years period from June 2006 to December 2009 in the Department of Orthopedics at Narayana Medical College, Nellore, Andhra Pradesh. This was a prospective study of nonunions of tibia treated by Ilizarov ring external fixators and incidental surgery.Results: Out of 20 tibial non-unions, in 15 patients the fixator has been removed. In 4 patients the fracture had united but the fixators are in situ and they are in consolidation phase and the remaining 1 patient is in various stage of follow up. The results of the 15 patients in whom the fixators have been removed are studied. The hospitalization time from 15 days to 120 days. The average hospital duration is 44.04 days. The total time ranged from 4 months to 15 months. Average time is 9.25 months.Conclusions: From this study we conclude that Ilizarov external fixator is a reliable, versatile and effective treatment for the treatment of tibia nonunion fractures

Temperature-dependent optical properties of plasmonic titanium nitride thin films

Due to their exceptional plasmonic properties, noble metals such as gold and silver have been the materials of choice for the demonstration of various plasmonic and nanophotonic phenomena. However, noble metals' softness, lack of tailorability and low melting point along with challenges in thin film fabrication and device integration have prevented the realization of real-life plasmonic devices.In the recent years, titanium nitride (TiN) has emerged as a promising plasmonic material with good metallic and refractory (high temperature stable) properties. The refractory nature of TiN could enable practical plasmonic devices operating at elevated temperatures for energy conversion and harsh-environment industries such as gas and oil. Here we report on the temperature dependent dielectric functions of TiN thin films of varying thicknesses in the technologically relevant visible and near-infrared wavelength range from 330 nm to 2000 nm for temperatures up to 900 0C using in-situ high temperature ellipsometry. Our findings show that the complex dielectric function of TiN at elevated temperatures deviates from the optical parameters at room temperature, indicating degradation in plasmonic properties both in the real and imaginary parts of the dielectric constant. However, quite strikingly, the relative changes of the optical properties of TiN are significantly smaller compared to its noble metal counterparts. Using simulations, we demonstrate that incorporating the temperature-induced deviations into the numerical models leads to significant differences in the optical responses of high temperature nanophotonic systems. These studies hold the key for accurate modeling of high temperature TiN based optical elements and nanophotonic systems for energy conversion, harsh-environment sensors and heat-assisted applications.Comment: 23 pages, 9 figures and 5 table

Three ploys for robust co-generation with generative adversarial nets

Generative adversarial nets (GANs) and variational auto-encoders enable accurate modeling of high-dimensional data distributions by forward propagating a sample drawn from a latent space. However, an often overlooked shortcoming is their inability to find an arbitrary marginal distribution, which is useful for completion of missing data in tasks like super-resolution, image inpainting, etc., where we don’t know the missing part ahead of time. To address such applications it seems intuitive at first to search for that latent space sample which ‘best’ matches the observations. However, irrespective of the GAN loss, unexpected challenges arise: we find that the energy landscape of well trained generators is extremely hard to optimize, exhibiting ‘folds’ that are very hard to overcome. To address this issue, in this thesis, three ploys are proposed which help to address the challenge for all investigated GAN losses and which yield more accurate reconstructions, quantitatively and qualitatively

Identification of physical processes via data driven methods

Extracting governing equations from data can be viewed as reverse engineering of Nature- using data to identify the physical laws/models. This approach is crucial for fields where data is abundant ( such as geophysical flows, finance, and neuroscience) but the physical laws based on the first principles are not available. In recent years, the use of machine learning (ML) methods complemented the need for formulating mathematical models through the application of data analysis algorithms that allow accurate estimation of observed dynamics by learning automatically from the given observations. The neural networks and symbolic regression (SR) based approaches are the most popular ML frameworks used to learn the underlying physical process by only the observing data. While neural network approaches have shown great promise, its black-box nature makes it difficult to interpret the learned models. On the other hand, symbolic regression algorithms are capable of learning/finding an analytically tractable function in symbolic form. Hence to address the functional expressibility, a key limitation of the black-box machine learning methods, this study has explored the use of symbolic regression approaches for identifying relations and operators that accurately represent the underlying physical processes. This study demonstrates the use of an evolutionary algorithm called gene expression programming (GEP) and a sparse optimization algorithm called sequential threshold ridge regression (STRidge) in discovering physical models. The effectiveness of these algorithms is demonstrated on four different applications: (1) partial differential equation (PDE) discovery, (2) truncation error analysis, (3) hidden physics discovery and (4 ) discovering subgrid-scale closure models. This study shows the GEP and STRidge algorithms are able to distill various linear/nonlinear PDEs, truncation error terms and unknown source terms of 1D and 2D PDEs. Furthermore, the classical Smagorinsky model is identified for subgrid-scale (SGS) closure from an array of tailored features in solving the 2D Kraichnan turbulence problem. Our results demonstrate the huge potential of these techniques in distilling complex nonlinear physics models from only observing the data. Furthermore, this study reveals the importance of feature selection/feature engineering and embedding the prior knowledge about the unknown dynamical system in terms of invariances for identifying models

A New EMAT Design for Generating Torsional Guided Wave Modes for Pipe Inspections

Guided waves inspection is a well-established method for the long-range ultrasonic inspection of pipes. Guided waves, used in a pulse-echo arrangement, can inspect a large range of the pipe from a single point as the pipe structure carries the waves over a large distance due to the relatively low attenuation of the wave modes. However, the complexity of the dispersion characteristics of these pipe guided wave modes are well known, and can lead to difficulty interpreting the obtained results. The torsional family of guided wave modes are generally considered to have much simpler dispersion characteristics; especially the fundamental T(0,1) mode, which is nominally non-dispersive, making it particularly useful for guided wave inspection. Torsional waves have been generated by a circumferential ring of transducers to approximate an axi-symmetric load to excite this T(0, 1) mode. Presented here is a new design of EMAT that can generate a T(0, 1) as a single transducer, rather than a circumferential array of transducers that all need to be excited in order to generate an axisymmetric force. The EMAT consists of a periodic permanent magnet array and a single meander coil, meaning that the excitation of the torsional mode is greatly simplified. The design parameters of this new EMAT are explored, and the ability to detect notch defects on a pipe is demonstrated

Effect of nutrient management on physio morphological and yield attributes of field pea (Pisum sativum L.)

A field experiment was conducted to investigate the impact of nitrogen management on the growth and yield of field peas. The experiment took place during the rabi season (October–March of 2022–2023) at Lovely Professional University's Agriculture Research Farm in Phagwara, Punjab, India. Fifteen different treatment combinations were utilized, involving the application of chemical fertilizers (NPK) and micronutrients (boron and zinc). The experimental design followed a randomized complete block approach with three replications. Among the treatment combinations, the application of foliar spray with B at a rate of 0.2%, Zn at a rate of 0.5%, along with 100% recommended dose of fertilizer (RDF), resulted in the highest measurements for plant height (70.44 cm), leaf count (70.60), branch count (18.86), leaf area (32.24 cm²), dry matter accumulation (6.12 g), crop growth rate (0.299 g m⁻² day⁻¹), and relative growth rate (0.05933 g g⁻¹ day⁻¹). Furthermore, treatments involving 100% RDF, 0.2% B, and 0.5% Zn exhibited enhanced yield characteristics, including the number of seeds per pod (10.26), pods per plant (12.33), test weight of seeds (15.06 g), seed yield (3537 kg ha⁻¹), and harvest index (47.49%). Furthermore, 100% RDF and the inclusion of 0.2% B and 0.5% Zn outperformed the control. Applying 100% RDF along with the micronutrients B and Zn is recommended to maximize production and net profit in field pea cultivation