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

The relationships of phenotype, genotype and some environmental factors with birth weight in Jersey calves

This study investigated the effects of parity, birth type, gender and birth season on birth weight in Jersey cattle and also investigated the relationships of phenotype and genotype with birth weight. Birth records of the Karakoy farm near Samsun, Turkey for the period from 1998 to 2005 were used as data for this study. Parity (P < 0.001), birth type (P < 0.001) and gender (P < 0.001) had a significant effect on birth weight, whereas season of calving and calving year (P > 0.05) were not significant. The birth weight mean of Jersey calves was 20.87 ± 1.79 kg. Total additive genotypic variance was 14.80, phenotypic variance was 38.95 and heritability of birth weight was 0.38. In planning a selection program to achieve ideal birth weight of Jersey calves, birth type, gender and parity need to be included, with the 4th or later parity been most influential.Key words: Birth weight, estimate of parameter, Jersey, parity, season of birth

Video Streaming using Message Accelerator

Virtual Network Computing or VNC is a largely used client application for accessing ?les and applications on remote computers. When there is high latency between the client and the server, VNC can undergo from major losses in throughput. These losses become obvious in the case of video, where updates are both large and continuous. Message Accelerator proxy for VNC is simple but highly effective solution for video performance while maintaining the advantages of a client-pull system. By operating on the server, it sends updates to the client at a rate corresponding to proxy-server interactions which are quicker than client-server interactions. When testing using video, our Message Accelerator design results in frame rates superior than plain VNC when running under high latency circumstances. Message Accelerator uses the pipelining system for updating the frames, which increases its performance to a great extent. Message Accelerator here is not a hardware part but software that we have to just apply in our video streaming program. DOI: 10.17762/ijritcc2321-8169.160413

Determinants of Symptomatic Vulvovaginal Candidiasis among Human Immunodeficiency Virus Type 1 Infected Women in Rural KwaZulu-Natal, South Africa

Introduction. We sought to determine the association between HIV-induced immunosuppression, virologic correlates, and vulvovaginal candidiasis (VVC). Methods. This is a retrospective cohort study, where HIV infected and uninfected women were studied with VVC being the primary outcome. Ninety-seven HIV-infected and 101 HIV-uninfected women were enrolled between June and December 2011. Cases of VVC were confirmed. HIV RNA load was determined by RT-PCR and CD4 counts were obtained from medical records. Results. Fifty-two of 97 (53.6%) HIV-infected and 38/101 (37.6%) HIV-uninfected women were diagnosed with VVC (P=0.032). The relative risk for VVC amongst HIV-infected patients was 1.53 (95% CI: 1.04–2 P=0.024). Cases of VVC increased at CD4+ T cell count below 200 cells/mm3 (P<0.0001) and plasma HIV RNA load above 10 000 copies/mL (P<0.0001). VVC was associated with increased genital shedding of HIV (P=0.002), and there was a linear correlation between plasma HIV load and genital HIV shedding (r=0.540; R2=0.292; P<0.0001). Women on HAART were 4-fold less likely (P=0.029) to develop VVC. Conclusion. CD4 counts below 200 cells/mm3 and plasma HIV loads ≥10 000 copies/mL were significantly associated with VVC

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

GaN epitaxy on thermally treated c-plane bulk ZnO substrates with O and Zn faces

ZnO is considered as a promising substrate for GaNepitaxy because of stacking match and close lattice match to GaN. Traditionally, however, it suffered from poor surface preparation which hampered epitaxialgrowth in general and GaN in particular. In this work, ZnO substrates with atomically flat and terrace-like features were attained by annealing at high temperature in air. GaNepitaxial layers on such thermally treated basal plane ZnO with Zn and O polarity have been grown by molecular beam epitaxy, and two-dimensional growth mode was achieved as indicated by reflection high-energy electron diffraction. We observed well-resolved ZnO and GaN peaks in the high-resolution x-ray diffraction scans, with no Ga2ZnO4 phase detectable. Low-temperature photoluminescence results indicate that high-quality GaN can be achieved on both O- and Zn-face ZnO

Excitonic fine structure and recombination dynamics in single-crystalline ZnO

The optical properties of a high quality bulk ZnO, thermally post treated in a forming gas environment are investigated by temperature dependent continuous wave and time-resolved photoluminescence (PL) measurements. Several bound and free exciton transitions along with their first excited states have been observed at low temperatures, with the main neutral-donor-bound exciton peak at 3.3605 eV having a linewidth of 0.7 meV and dominating the PL spectrum at 10 K. This bound exciton transition was visible only below 150 K, whereas the A-free exciton transition at 3.3771 eV persisted up to room temperature. A-free exciton binding energy of 60 meV is obtained from the position of the excited states of the free excitons. Additional intrinsic and extrinsic fine structures such as polariton, two-electron satellites, donor-acceptor pair transitions, and longitudinal optical-phonon replicas have also been observed and investigated in detail. Time-resolved PL measurements at room temperature reveal a biexponential decay behavior with typical decay constants of similar to170 and similar to864 ps for the as-grown sample. Thermal treatment is observed to increase the carrier lifetimes when performed in a forming gas environment

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