Using recurrent neural networks to detect changes in autocorrelated processes for quality monitoring

With the growing of automation in manufacturing, process quality characteristics are being measured at higher rates and data are more likely to be autocorrelated. A widely used approach for statistical process monitoring in the case of autocorrelated data is the residual chart. This chart requires that a suitable model has been identified for the time series of process observations before residuals can be obtained. In this work, a new neural-based procedure, which is alleviated from the need for building a time series model, is introduced for quality control in the case of serially correlated data. In particular, the Elman’s recurrent neural network is proposed for manufacturing process quality control. Performance comparisons between the neural-based algorithm and several control charts are also presented in the paper in order to validate the approach. Different magnitudes of the process mean shift, under the presence of various levels of autocorrelation, are considered. The simulation results indicate that the neural-based procedure may perform better than other control charting schemes in several instances for both small and large shifts. Given the simplicity of the proposed neural network and its adaptability, this approach is proved from simulation experiments to be a feasible alternative for quality monitoring in the case of autocorrelated process data.     

A 0.18 μm CMOS direct down-converter for highly integrated 3G receivers

This paper presents the design of a high dynamic range direct down-converter for 3G cell-phone applications. The mechanisms responsible for second-order intermodulation distortion are discussed in details, leading to the following design strategy: the transconductor is degenerated by means of an RC filter, an LC network resonating at RF frequency loads the switching pair and carefully matched resistors are used in the output load. Prototypes realized in 0.18 μm CMOS show: +78 dBm IIP2 minimum among 40 samples, +10 dBm IIP3, 4 nV/√Hz input-referred noise density while burning only 4 mA from 1.8 V.     

Efficient breadth first search on multi-GPU systems

Simple algorithms for the execution of a Breadth First Search on large graphs lead, running on clusters of GPUs, to a situation of load unbalance among threads and un-coalesced memory accesses, resulting in pretty low performances. To obtain a significant improvement on a single GPU and to scale by using multiple GPUs, we resort to a suitable combination of operations to rearrange data before processing them. We propose a novel technique for mapping threads to data that achieves a perfect load balance by leveraging prefix-sum and binary search operations. To reduce the communication overhead, we perform a pruning operation on the set of edges that needs to be exchanged at each BFS level. The result is an algorithm that exploits at its best the parallelism available on a single GPU and minimizes communication among GPUs. We show that a cluster of GPUs can efficiently perform a distributed BFS on graphs with billions of nodes.     

Cultural ‘demons’ as future builders

Usually, the shape of the future is seen as the result of a cultural flow that, according to some privileged cultural variable, like technology, goes undisturbed towards its own outcome. This is a quite naive attitude that has been very rarely successful. Both conventional technology and technology of the artificial show that, within culture, ‘demons’ are always active trying to exploit or even bypass standards in order to give birth to unexpected novelties. This is true within the pure technology area and even more in its artistic extensions. Therefore, no deterministic process is going on but, rather, a permanent remixing of chances that makes any prophecy impossible.     

High-rate quantum key distribution at short wavelength: Performance analysis and evaluation of silicon single photon avalanche diodes

Abstract

Experimental results obtained with silicon single photon avalanche diodes (SPADs) in quantum key distribution (QKD) at short wavelengths reveal remarkable potential for application in local area networks (LAN) and for free-space transmission at high rate. Actual application prospects, however, depend on the performance level and on the suitability of practical systems using the available silicon SPAD devices. They can be essentially divided in two groups: planar p-n junction structures with a thin depletion layer (typically 1 μm); and reach-through structures with a thick depletion layer (from 20 μm to 150μm). The physical mechanisms that control the device behaviour were investigated and the effect on the key parameters of the detector (quantum detection efficiency, dark counting rate, afterpulsing probability and photon-timing jitter) were thoroughly assessed. A quantitative analysis was made of the influence of such parameters on the quantum bit error rate (QBER). Actual parameters were measured and the attainable performance and system suitability of the two device types evaluated. Comparable performance is obtained, but from a system viewpoint thin SPADs appear inherently better suited to high-rate QKD applications, because of their faster response time, ruggedness, low voltage, low power dissipation and fabrication technology, which is simple, efficient, economical and compatible with monolithic integration of detector and associated circuits.     

Experimental Study of an Adding Interferometer at Millimeter Waves

In order to study an original detection architecture for future cosmology experiments based on wide band adding interferometry, we have tested a single baseline bench instrument based on commercial components. The instrument has been characterized in the laboratory with a wide band power detection setup. A method which allows us to reconstruct the complete transfer function of the interferometer has been developed and validated with measurements. This scheme is useful to propagate the spurious effects of each component till the output of the detector.     

Multi-rate optimizing control of simulated moving beds

This paper presents an optimizing control scheme for simulated moving beds (SMB) that enables to incorporate multi-rate (MR) sampled measurements into the control and estimation problem in a clear and transparent manner. This is particularly relevant for chiral separations where online monitoring requires the combination of various analytical techniques that may operate on widely varying time scales. An MR periodic linear time-varying (PLTV) model is derived for the SMB process. The cyclic nature of the process is exploited by formulating the MR-PLTV model within a repetitive model predictive control framework. Simulation results for the chiral separation of the guaifenesin enantiomers are presented. The proposed multi-rate controller is able to deliver increased productivity while respecting the process and product specifications.     

Regulatory Noncoding and Predicted Pathogenic Coding Variants of CCR5 Predispose to Severe COVID-19

Genome-wide association studies (GWAS) found locus 3p21.31 associated with severe COVID-19. CCR5 resides at the same locus and, given its known biological role in other infection diseases, we investigated if common noncoding and rare coding variants, affecting CCR5, can predispose to severe COVID-19. We combined single nucleotide polymorphisms (SNPs) that met the suggestive significance level (P ≤ 1 × 10⁻⁵) at the 3p21.31 locus in public GWAS datasets (6406 COVID-19 hospitalized patients and 902,088 controls) with gene expression data from 208 lung tissues, Hi-C, and Chip-seq data. Through whole exome sequencing (WES), we explored rare coding variants in 147 severe COVID-19 patients. We identified three SNPs (rs9845542, rs12639314, and rs35951367) associated with severe COVID-19 whose risk alleles correlated with low CCR5 expression in lung tissues. The rs35951367 resided in a CTFC binding site that interacts with CCR5 gene in lung tissues and was confirmed to be associated with severe COVID-19 in two independent datasets. We also identified a rare coding variant (rs34418657) associated with the risk of developing severe COVID-19. Our results suggest a biological role of CCR5 in the progression of COVID-19 as common and rare genetic variants can increase the risk of developing severe COVID-19 by affecting the functions of CCR5.     

Parallel computer vision on Polymorphic Torus architecture

Polymorphic Torus is a novel interconnection network for SIMD massively parallel computers, able to support effectively both local and global communication. Thanks to this characteristic, Polymorphic Torus is highly suitable for computer vision applications, since vision involves local communication at the low-level stage and global communication at the intermediate- and high-level stages. In this paper we evaluate the performance of Polymorphic Torus in the computer vision domain. We consider a set of basic vision tasks, namely,convolution, histogramming, connected component labeling, Hough transform, extreme point identification, diameter computation, andvisibility, and show how they can take advantage of the Polymorphic Torus communication capabilities. For each basic vision task we propose a Polymorphic Torus parallel algorithm, give its computational complexity, and compare such a complexity with the complexity of the same task inmesh, tree, pyramid, and hypercube interconnection networks. In spite of the fact that Polymorphic Torus has the same wiring complexity as mesh, the comparison shows that in all of the vision tasks under examination it achieves complexity lower than or at most equal to hypercube, which is the most powerful among the interconnection networks considered.     

Nuclear data sensitivity/uncertainty analysis for XT-ADS

The XT-ADS, an accelerator-driven system for an experimental demonstration, has been investigated in the framework of IP EUROTRANS FP6 project. In this study, the sensitivity and uncertainty analyses were performed to comprehend the reliability of the XT-ADS neutronic design.