RF Power Amplifier Behavioral Modeling Based on Takenaka–Malmquist–Volterra Series

In this paper, a Takenaka–Malmquist–Volterra (TMV) model structure is employed to improve the approximations in the low-pass equivalent behavioral modeling of radio frequency (RF) power amplifiers (PAs). The Takenaka–Malmquist basis generalizes the orthonormal basis functions previously used in this context. In addition, it allows each nonlinearity order in the expanded Volterra model to be parameterized by multiple complex poles (dynamics). The state-space realizations for the TMV models are introduced. The pole sets for the TMV model and also for the previous Laguerre–Volterra (LV) and Kautz–Volterra (KV) models are obtained using a constrained nonlinear optimization approach. Based on experimental data measured on a GaN HEMT class AB RF PA excited by a WCDMA signal, it is observed that the TMV model reduces the normalized mean-square error and the adjacent channel error power ratio for the upper adjacent channel (upper ACEPR) by 1.6 dB when it is compared to the previous LV and KV models under the same computational complexity.     

Influence of grape composition and winemaking on the anthocyanin composition of red wines of Tannat

The anthocyanin composition of Tannat red wines obtained with four winemaking techniques was evaluated in different years. The wines were elaborated with traditional maceration (TM), cold pre‐fermentative maceration, delayed extraction of anthocyanins and extended maceration. Two vinifications were carried out for each technique, employing 70 kg of grapes in each one. The anthocyanin composition of wines was analysed by high‐performance liquid chromatography–diode array detection. The results confirm the fundamental role of the grape composition on the anthocyanin composition of the wines. The winemaking had an important effect on the total concentration of anthocyanins and also may modify the anthocyanic profile of wines. The wines obtained with TM had higher or similar content of anthocyanins than those elaborated with the other techniques. Canonical discriminant analysis of data showed that vintage has a greater effect on anthocyanin concentration than winemaking techniques.     

Influence of an exopolysaccharide produced by a starter on milk coagulation and curd syneresis

The objective of this study was to assess the effect of an exopolysaccharide-producing starter culture on milk coagulation and curd syneresis during the manufacture of half-fat Cheddar cheese by comparing the effect of an exopolysaccharide-producing starter with its non exopolysaccharide-producing isogenic variant. Coagulation and syneresis were monitored using light backscatter sensor technologies and the charge of the exopolysaccharide was also established. The distribution of the exopolysaccharide within the cheese microstructure was investigated by staining with Concavalin A and using confocal laser scanning microscopy. The study indicated that the exopolysaccharide produced by this starter culture did not interfere with coagulation but had a significant effect on reducing syneresis shortly after cutting. No charge was found on the exopolysaccharide under the measurement conditions. The exopolysaccharide was observed to be uniformly distributed throughout the cheese matrix and specifically located near the aqueous pores, possibly binding moisture and causing the observed decrease in syneresis.     

Algorithm transformation methods to reduce the overhead of software-based fault tolerance techniques

This paper introduces a framework that tackles the costs in area and energy consumed by methodologies like spatial or temporal redundancy with a different approach: given an algorithm, we find a transformation in which part of the computation involved is transformed into memory accesses. The precomputed data stored in memory can be protected then by applying traditional and well established ECC algorithms to provide fault tolerant hardware designs. At the same time, the transformation increases the performance of the system by reducing its execution time, which is then used by customized software-based fault tolerant techniques to protect the system without any degradation when compared to its original form. Application of this technique to key algorithms in a MP3 player, combined with a fault injection campaign, show that this approach increases fault tolerance up to 92%, without any performance degradation.     

Development of a novel device for applying uniform doses of electron beam irradiation on carcasses

The Maxim's Electron Scatter Chamber (Maxim Chamber) was developed to obtain uniform dose distribution when applying electron beam (e-beam) irradiation to materials of irregular surface. This was achieved by placing a stainless steel mesh surrounding a cylindrical area where the target sample was placed. Upon contact with the mesh, electrons scatter and are directed onto the target from multiple angles, eliminating the e-beam linearity and resulting in a uniform dose distribution over the target surface. The effect of irradiation in the Maxim Chamber on dose distribution and pathogen reduction was tested on rabbit carcasses to simulate other larger carcasses. The dose uniformity ratio (DUR) on the rabbit carcasses was 1.8, indicating an acceptable dose distribution. On inoculated carcasses, this treatment reduced Escherichia coli O157:H7 by > 5 log cycles. These results indicate that carcass irradiation using e-beam is feasible using the Maxim's electron scattering chamber. Appropriate adjustments will be further needed for commercial application on beef and other animal carcasses.     

A Linear Optimal Transportation Framework for Quantifying and Visualizing Variations in Sets of Images

Transportation-based metrics for comparing images have long been applied to analyze images, especially where one can interpret the pixel intensities (or derived quantities) as a distribution of ‘mass’ that can be transported without strict geometric constraints. Here we describe a new transportation-based framework for analyzing sets of images. More specifically, we describe a new transportation-related distance between pairs of images, which we denote as linear optimal transportation (LOT). The LOT can be used directly on pixel intensities, and is based on a linearized version of the Kantorovich-Wasserstein metric (an optimal transportation distance, as is the earth mover’s distance). The new framework is especially well suited for computing all pairwise distances for a large database of images efficiently, and thus it can be used for pattern recognition in sets of images. In addition, the new LOT framework also allows for an isometric linear embedding, greatly facilitating the ability to visualize discriminant information in different classes of images. We demonstrate the application of the framework to several tasks such as discriminating nuclear chromatin patterns in cancer cells, decoding differences in facial expressions, galaxy morphologies, as well as sub cellular protein distributions.     

A Markovian–Bayesian Network for Risk Analysis of High Speed and Conventional Railway Lines Integrating Human Errors

The article provides a new Markovian–Bayesian network model to evaluate the probability of accident associated with the circulation of trains along a given high speed or conventional railway line with special consideration to human error. This probability increases as trains pass throughout the different elements encountered along the line. A Bayesian network, made up of a sequence of several connected Bayesian subnetworks, is used. A subnetwork is associated with each element in the line that implies a concentrated risk of accident or produces a change in the driver's attention, such as signals, tunnel, or viaduct entries or exits, etc. Bayesian subnetworks are also used to reproduce segments without signals where some elements add continuous risks, such as rolling stock failures, falling materials, slope slides in cuttings and embankments, etc. All subnetworks are connected with the previous one and some of them are multi‐connected because some consequences are dependent on previous errors. Because driver's attention plays a crucial role, its degradation with driving time and the changes due to seeing light signals or receiving acoustic signals is taken into consideration. The model updates the driver's attention level and accumulates the probability of accident associated with the different elements encountered along the line. This permits us to generate a continuously increasing risk graph that includes continuous and sudden changes indicating where the main risks appear and whether or not an action must be taken by the infrastructure manager. Sensitivity analysis allows the relevant and irrelevant parameters to be identified avoiding wastes of time and money by concentrating safety improvement actions only on the relevant ones. Finally, some examples are used to illustrate the model. In particular, the case of the Orense–Santiago de Compostela line, where a terrible accident took place in 2013.     

An Improved NER Methodology to the Portuguese Language

Mobile Networks and Applications - The text mining process typically involves the application of natural language processing (NLP) techniques, in order to obtain important information and extract...