A robust compressive sensing based technique for reconstruction of sparse radar scenes

Cataloged from PDF version of article.Pulse-Doppler radar has been successfully applied to surveillance and tracking of both moving and stationary targets. For efficient processing of radar returns, delay–Doppler plane is discretized and FFT techniques are employed to compute matched filter output on this discrete grid. However, for targets whose delay–Doppler values do not coincide with the computation grid, the detection performance degrades considerably. Especially for detecting strong and closely spaced targets this causes miss detections and false alarms. This phenomena is known as the off-grid problem. Although compressive sensing based techniques provide sparse and high resolution results at sub-Nyquist sampling rates, straightforward application of these techniques is significantly more sensitive to the off-grid problem. Here a novel parameter perturbation based sparse reconstruction technique is proposed for robust delay– Doppler radar processing even under the off-grid case. Although the perturbation idea is general and can be implemented in association with other greedy techniques, presently it is used within an orthogonal matching pursuit (OMP) framework. In the proposed technique, the selected dictionary parameters are perturbed towards directions to decrease the orthogonal residual norm. The obtained results show that accurate and sparse reconstructions can be obtained for off-grid multi target cases. A new performance metric based on Kullback–Leibler Divergence (KLD) is proposed to better characterize the error between actual and reconstructed parameter spaces. Increased performance with lower reconstruction errors are obtained for all the tested performance criteria for the proposed technique compared to conventional OMP and 1 minimization techniques. © 2013 Elsevier Inc. All rights reserve

Sparse ground-penetrating radar imaging method for off-the-grid target problem

Cataloged from PDF version of article.Spatial sparsity of the target space in subsurface or through-the-wall imaging applications has been successfully used within the compressive-sensing framework to decrease the data acquisition load in practical systems, while also generating high-resolution images. The developed techniques in this area mainly discretize the continuous target space into grid points and generate a dictionary of model data that is used in image-reconstructing optimization problems. However, for targets that do not coincide with the computation grid, imaging performance degrades considerably. This phenomenon is known as the off-grid problem. This paper presents a novel sparse ground-penetrating radar imaging method that is robust for off-grid targets. The proposed technique is an iterative orthogonal matching pursuit-based method that uses gradient-based steepest ascent-type iterations to locate the off-grid target. Simulations show that robust results with much smaller reconstruction errors are obtained for multiple off-grid targets compared to standard sparse reconstruction techniques. (c) 2013 SPIE and IS&

Perturbed Orthogonal Matching Pursuit

Cataloged from PDF version of article.Compressive Sensing theory details how a sparsely represented signal in a known basis can be reconstructed with an underdetermined linear measurement model. However, in reality there is a mismatch between the assumed and the actual bases due to factors such as discretization of the parameter space defining basis components, sampling jitter in A/D conversion, and model errors. Due to this mismatch, a signal may not be sparse in the assumed basis, which causes significant performance degradation in sparse reconstruction algorithms. To eliminate the mismatch problem, this paper presents a novel perturbed orthogonal matching pursuit (POMP) algorithm that performs controlled perturbation of selected support vectors to decrease the orthogonal residual at each iteration. Based on detailed mathematical analysis, conditions for successful reconstruction are derived. Simulations show that robust results with much smaller reconstruction errors in the case of perturbed bases can be obtained as compared to standard sparse reconstruction techniques

Population, Morphological and Biochemical Characterization of Microorganism in Plantain Root across different Farmlands in Toru-Orua Metropolis, Bayelsa State, Nigeria

This paper assessed the population, morphological and biochemical characterization of microorganism in plantain root across different farmlands in Toru-Orua Metropolis, Bayelsa State, Nigeria using standard methods. Data obtained show that microbial population count ranged from 1 × 107 - 9 × 107 (cfu/g) in the study area, while fungi isolated include Aspergillus fumigatus, Aspergillus niger and Aspergillus flavus. These are the largest of all microorganisms in the soil. Others were Rhizobium, Nitrobacter, Winogradkyl, Azomona argillis and Psydormonads aeruginosa. Practices that would enhance nutrition of the plants and the proliferation of bacteria and fungi around the roots of plantain are recommended such as organic matter accumulation in form of green manuring, zero tillage and non-use of chemicals and burning

Stimulated emission and time-resolved photoluminescence in rf-sputtered ZnO thin films

Stimulated emission (SE) was measured from ZnOthin filmsgrown on c-plane sapphire by rf sputtering. Free exciton transitions were clearly observed at 10 K in the photoluminescence(PL), transmission, and reflection spectra of the sample annealed at 950 °C. SE resulting from both exciton-exciton scattering and electron hole plasma formation was observed in the annealed samples at moderate excitation energy densities. The SE threshold energy density decreased with increasing annealing temperature up to ∼950 °C. The observation of low threshold exciton-exciton scattering-induced SE showed that excitonic laser action could be obtained in rf-sputtered ZnOthin films. At excitation densities below the SE threshold, time-resolvedPL revealed very fast recombination times of ∼74 ps at room temperature, and no significant change at 85 K. The decay time for the SE-induced PL was below the system resolution of \u3c45 ps

Modeling of the Impact of Initial Mold Temperature, Al5Ti1B and Al10Sr Additions on the Critical Fraction of Solid in Die Casting of Aluminum Alloys using Fuzzy Expert System

In the casting of liquid metal, the feeding stops when the mushy zone is clogged and does not allow the transfer of feeding liquid. The growing resistance of the solid dendrites against the fluidity of the feeding liquid is defined as the critical fraction of solid (CFS). CFS value varies depending on many factors such as alloy solidification range, initial mold temperature, and the grain size. Therefore, in many casting simulation applications, it is quite common to get inconsistent results due to insufficient information about the CFS. In this study, a fuzzy expert system (FES) model has been developed in order to determine the value of the CFS in the die casting process, based on the parameters of the alloy type, the initial mold temperature, Al5Ti1B addition and Al10Sr addition. In order to create the rule base for the FES model, 54 die casting experiments have been carried out. The CFS values obtained using the FES model has revealed that the developed model of the FES predicts the CFS value in a high performance

Leveraging artificial intelligence in vaccine development: A narrative review

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases

A comprehensive review of ZnO materials and devices

The semiconductor ZnO has gained substantial interest in the research community in part because of its large exciton binding energy (60 meV) which could lead to lasing action based on exciton recombination even above room temperature. Even though research focusing on ZnO goes back many decades, the renewed interest is fueled by availability of high-quality substrates and reports of p-type conduction and ferromagnetic behavior when doped with transitions metals, both of which remain controversial. It is this renewed interest in ZnO which forms the basis of this review. As mentioned already, ZnO is not new to the semiconductor field, with studies of its lattice parameter dating back to 1935 by Bunn [Proc. Phys. Soc. London 47, 836 (1935)], studies of its vibrational properties with Raman scattering in 1966 by Damen et al. [Phys. Rev.142, 570 (1966)], detailed optical studies in 1954 by Mollwo [Z. Angew. Phys.6, 257 (1954)], and its growth by chemical-vapor transport in 1970 by Galli and Coker [Appl. Phys. Lett.16, 439 (1970)]. In terms of devices, Au Schottky barriers in 1965 by Mead [Phys. Lett.18, 218 (1965)], demonstration of light-emitting diodes (1967) by Drapak [Semiconductors 2, 624 (1968)], in which Cu2O was used as the p-type material, metal-insulator-semiconductor structures (1974) by Minami et al. [Jpn. J. Appl. Phys.13, 1475 (1974)], ZnO∕ZnSe n-p junctions (1975) by Tsurkan et al. [Semiconductors 6, 1183 (1975)], and Al∕Au Ohmic contacts by Brillson [J. Vac. Sci. Technol.15, 1378 (1978)] were attained. The main obstacle to the development of ZnO has been the lack of reproducible and low-resistivity p-type ZnO, as recently discussed by Look and Claflin [Phys. Status Solidi B241, 624 (2004)]. While ZnO already has many industrial applications owing to its piezoelectric properties and band gap in the near ultraviolet, its applications to optoelectronic devices has not yet materialized due chiefly to the lack of p-type epitaxial layers. Very high quality what used to be called whiskers and platelets, the nomenclature for which gave way to nanostructures of late, have been prepared early on and used to deduce much of the principal properties of this material, particularly in terms of optical processes. The suggestion of attainment of p-type conductivity in the last few years has rekindled the long-time, albeit dormant, fervor of exploiting this material for optoelectronic applications. The attraction can simply be attributed to the large exciton binding energy of 60 meV of ZnO potentially paving the way for efficient room-temperature exciton-based emitters, and sharp transitions facilitating very low threshold semiconductor lasers. The field is also fueled by theoretical predictions and perhaps experimental confirmation of ferromagnetism at room temperature for potential spintronics applications. This review gives an in-depth discussion of the mechanical, chemical, electrical, and optical properties of ZnO in addition to the technological issues such as growth, defects, p-type doping, band-gap engineering, devices, and nanostructures

4H–SiC photoconductive switching devices for use in high-power applications

Siliconcarbide is a wide-band-gapsemiconductor suitable for high-power high-voltage devices and it has excellent properties for use in photoconductive semiconductor switches (PCSSs). PCSS were fabricated as planar structures on high-resistivity 4H–SiC and tested at dc bias voltages up to 1000 V. The typical maximum photocurrent of the device at 1000 V was about 49.4 A. The average on-state resistance and the ratio of on-state to off-state currents were about 20 Ω and 3×1011, respectively. Photoconductivity pulse widths for all applied voltages were 8–10 ns. These excellent results are due in part to the removal of the surface damage by high-temperature H2 etching and surface preparation. Atomic force microscopy images revealed that very good surface morphology, atomic layer flatness, and large step width were achieved