Concentric circles and spiral configurations for large correlator arrays in radio astronomy

Aperture synthesis arrays are commonly used in radio astronomy to take images of radio point sources, with the planned Square Kilometre Array (SKA) being the most common example. One approach to enhancing the quality of the images is to optimize an antenna array configuration in a possible SKA implementation. An ideal arrangement must ensure optimal configurations to capture a clear image by either decreasing the sidelobe level (SLL) in the l-m domain or increasing the sampled data in the spatial-frequency domain. In this paper a novel configuration is considered to optimize the array by considering all possible observation situations through the positions of the antenna array elements via a mathematical model that we call geometrical method (GM). To demonstrate its efficiency, the technique is applied to developing an optimal configuration for the elements of the Giant Metrewave Radio Telescope (GMRT). The effect of these changes, particularly in the forms of circular and spiral arrangements, is discussed. It is found that a spiral configuration results in fewer overlapping samples than the number of antennas placed along three arms of the GMRT with fewer than 11% and 27% overlapping samples in the snapshot and 6 hr tracking observations, respectively. Finally, the spiral configuration reduces the first SLL from -13.01 dB, using the arms of the current GMRT configuration, to - 15.64 dB.Web of Science1564art. no. 17

Low-Complexity Robust Beamforming Design for IRS-Aided MISO Systems with Imperfect Channels

In this paper, large-scale intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) system is considered in the presence of channel uncertainty. To maximize the average sum rate of the system by jointly optimizing the active beamforming at the BS and the passive phase shifts at the IRS, while satisfying the power constraints, a novel robust beamforming design is proposed by using the penalty dual decomposition (PDD) algorithm. By applying the upper bound maximization/minimization (BSUM) method, in each iteration of the algorithm, the optimal solution for each variable can be obtained with closed-form expression. Simulation results show that the proposed scheme achieves high performance with very low computational complexity

Robust Beamforming Design for an IRS-Aided NOMA Communication System With CSI Uncertainty

Intelligent reflecting surface (IRS) is a promising technology that provides high throughput in future communication systems and is compatible with various communication techniques, such as non-orthogonal multiple-access (NOMA). This paper studies the downlink transmission of IRS-assisted NOMA communication, considering the practical case of imperfect channel state information (CSI). Aiming to maximize the system sum rate, a robust IRS-aided NOMA design is proposed to jointly find the optimal beamforming vector for the access point and the passive reflection matrix for the IRS. This robust design is realised using the penalty dual decomposition (PDD) scheme, and it is shown that the results have a close performance to their upper bound obtained from the corresponding perfect CSI scenario. The presented method is compatible with both continuous and discrete phase shift elements of the IRS. Our findings show that the proposed algorithms, for both continuous and discrete IRS, have low computational complexity compared to other schemes in the literature. Furthermore, we conduct a performance comparison between the IRS-aided NOMA and the IRS-aided orthogonal multiple access (OMA). This comparison shows that robust beamforming techniques are crucial for the system to reap the advantages of IRS-aided NOMA communication in the presence of CSI uncertainty

A Trellis-based Passive Beamforming Design for an Intelligent Reflecting Surface-Aided MISO System

In this paper, the downlink transmission of an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) system is investigated where the IRS elements are selected from a predefined discrete set of phase shifts. We minimize the mean square error (MSE) of the received symbols in the system via optimizing the phase shifts at the IRS jointly with beamforming vectors at the base station (BS) and equalizers at the user terminals. In order to find the optimal IRS phase shifts, a trellis-based structure is used that smartly selects the discrete phases. Moreover, for the sake of comparison, a semi-definite programming (SDP)-based discrete phase optimization is also presented. The BS beamformer and the optimal equalizers are determined via closed-form solutions. Numerical results demonstrate that the trellis-based scheme has better performance compared to other discrete IRS phase shift designs, such as SDP and quantized majorization-minimization technique, while maintaining a very low computational complexity

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

Tobacco smoking, polymorphisms in carcinogen metabolism enzyme genes, and risk of localized and advanced prostate cancer: results from the California Collaborative Prostate Cancer Study

The relationship between tobacco smoking and prostate cancer (PCa) remains inconclusive. This study examined the association between tobacco smoking and PCa risk taking into account polymorphisms in carcinogen metabolism enzyme genes as possible effect modifiers (9 polymorphisms and 1 predicted phenotype from metabolism enzyme genes). The study included cases (n = 761 localized; n = 1199 advanced) and controls (n = 1139) from the multiethnic California Collaborative Case–Control Study of Prostate Cancer. Multivariable conditional logistic regression was performed to evaluate the association between tobacco smoking variables and risk of localized and advanced PCa risk. Being a former smoker, regardless of time of quit smoking, was associated with an increased risk of localized PCa (odds ratio [OR] = 1.3; 95% confidence interval [CI] = 1.0–1.6). Among non-Hispanic Whites, ever smoking was associated with an increased risk of localized PCa (OR = 1.5; 95% CI = 1.1–2.1), whereas current smoking was associated with risk of advanced PCa (OR = 1.4; 95% CI = 1.0–1.9). However, no associations were observed between smoking intensity, duration or pack-year variables, and advanced PCa. No statistically significant trends were seen among Hispanics or African-Americans. The relationship between smoking status and PCa risk was modified by the CYP1A2 rs7662551 polymorphism (P-interaction = 0.008). In conclusion, tobacco smoking was associated with risk of PCa, primarily localized disease among non-Hispanic Whites. This association was modified by a genetic variant in CYP1A2, thus supporting a role for tobacco carcinogens in PCa risk

Web Mining for Web Personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented