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In this paper, the clamping effect introduced by several diode-based configurations used for the implementation of the high-value resistors in quasi-floating-gate circuits is analyzed and characterized. In contrast to previous approaches where a parasitic diode is treated as a simple high-value resistor reducing the circuit complexity, in this case the analysis considers the diode behavior which leads to a clamping circuit. This clamping circuit introduces an unwanted amplitude-dependent offset voltage, which affects the performance moving the quiescent point at the quasi-floating-gate transistors. A new anti-parallel diode configuration for quasi-floating-gate applications is proposed in this work, which eliminates this unwanted offset voltage. The proposed design is validated using simulations and experimental data in a CMOS 0.35- μ m technology. [ABSTRACT FROM AUTHOR]

Given an undirected connected graph G = (V, E), a subset of vertices S is a maximum 2-packing set if the number of edges in the shortest path between any pair of vertices in S is at least 3 and S has the maximum cardinality. In this paper, we present a genetic algorithm for the maximum 2-packing set problem on arbitrary graphs, which is an NP-hard problem. To the best of our knowledge, this work is a pioneering effort to tackle this problem for arbitrary graphs. For comparison, we extended and outperformed a well-known genetic algorithm originally designed for the maximum independent set problem. We also compared our genetic algorithm with a polynomial-time one for the maximum 2-packing set problem on cactus graphs. Empirical results show that our genetic algorithm is capable of finding 2-packing sets with a cardinality relatively close (or equal) to that of the maximum 2-packing sets. Moreover, the cardinality of the 2-packing sets found by our genetic algorithm increases linearly with the number of vertices and with a larger population and a larger number of generations. Furthermore, we provide a theoretical proof demonstrating that our genetic algorithm increases the fitness for each candidate solution when certain conditions are met.

This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in color images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the color image channels. The correction stage consists of a vector median filter version that modifies color channel values if some are noisy. An experimental study was performed to compare the proposed filter with others in the state-of-the-art related to color image denoising. Their performance was measured objectively through the image quality metrics PSNR, MAE, SSIM, and FSIM. Experimental findings reveal substantial variability among filters based on noise model and image characteristics. The findings also indicate that, on average, the proposed filter consistently exhibited top-tier performance values for the three impulse noise models, surpassed only by a filter employing a deep learning-based approach. Unlike deep learning filters, which are black boxes with internal workings invisible to the user, the proposed filter has a high interpretability with a performance close to an equilibrium point for all images and noise models used in the experiment. [ABSTRACT FROM AUTHOR]

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A ridesharing system is a transport mode where two or more users share the same vehicle and divide the trip's expenses based on similar routes and itineraries. Popular ridesharing systems, such as Uber, Flinc, and Lyft, define a matching among users based only on the coincidence of routes. However, these systems do not guarantee a stable matching (i.e., a matching in which no user prefers another different from the assigned one). In this work, a new ridesharing system model is proposed, including three types of trips: identical, inclusive, and partial. This model is used to introduce a new algorithm to address the stable matching problem for ridesharing systems. Finally, a set of experimental simulations of the proposed algorithm is conducted. Experimental results show that the proposed algorithm always produces a stable matching. [ABSTRACT FROM AUTHOR]

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Traditional software teams that implement agile methodologies frequently experience difficulty working efficiently as agile teams. While agile methodologies support and promote social activities and behaviors, the understanding of the social phenomena and challenges that arise during the transformation of traditional teams into agile teams is limited. We conducted an extensive 10‐month case study in a software enterprise to analyze the changes in the communication and social interactions of employees during the transformation process, and to delineate the social challenges that arise during this process. Using social interaction registers, we collected data on the communication and social interactions of employees, first when they worked as a traditional team and then as an agile team. Then, we processed the data using social network analysis methods to identify the differences between these 2 types of teams and to create graphs of communication and social interaction networks. We identified that the transformation process influences formal, informal, oral, and written communication and increases conjunctive and disjunctive social interactions. Further, from the results of interviews conducted with all the enterprise members, we identified 5 social challenges to transforming a traditional team into an agile one. The results of this research suggest that the transformation process requires broad changes in the relationships and interactions among employees. [ABSTRACT FROM AUTHOR]

We address the bodyguard allocation problem (BAP), an optimization problem that illustrates the conflict of interest between two classes of processes with contradictory preferences within a distributed system. While a class of processes prefers to minimize its distance to a particular process called the root, the other class prefers to maximize it; at the same time, all the processes seek to build a communication spanning tree with the maximum social welfare. The two state-of-the-art algorithms for this problem always guarantee the generation of a spanning tree that satisfies a condition of Nash equilibrium in the system; however, such a tree does not necessarily produce the maximum social welfare. In this paper, we propose a two-player coalition cooperative scheme for BAP, which allows some processes to perturb or break a Nash equilibrium to find another one with a better social welfare. By using this cooperative scheme, we propose a new algorithm called FFC-BAPS for BAP. We present both theoretical and empirical analyses which show that this algorithm produces better quality approximate solutions than former algorithms for BAP. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Bodyguard Allocation Problem (BAP) game, that illustrates the behavior of processes with contradictory individual goals in distributed systems. In particular, the game deals with the conflict of interest between two classes of processes that maximize/minimize their distance to a special process called the root. A solution of the BAP game represents a rooted spanning tree in which there exists a condition of equilibrium with maximum social welfare. We analyze the inefficiency of equilibria of the game based on both a completely cooperative and noncooperative approach. Additionally, we design two algorithms, CBAP and DBAP, that provide approximated solutions for the BAP game. We prove that both algorithms always terminate in a configuration with equilibrium and we analyze their running time based on the approach of cooperation used. We perform experimental simulations to compare the overall quality of equilibria obtained by the proposed algorithms. [ABSTRACT FROM AUTHOR]

The performance of processors in a distributed system can be measured by parameters such as bandwidth, storage capacity, work capability, reliability, power limitations, years of usage, among others. Each processor defines its preference based on these parameters. The preference represents an indicator of the quality of service that a processor can provide. An algorithm that follows a preference-based approach uses the preference of the processors to make decisions. In this paper we introduce a randomized self-stabilizing leader election algorithm for preference-based anonymous trees. Our algorithm assures that the processor with the highest preference in the system is always selected as the leader; moreover, it is able to solve symmetric configurations where each preference is the same. We prove that our algorithm has an optimal average time complexity and we also performed simulations to illustrate the average performance of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper we present a simulation of an acyclic n×n DR-Mesh on an n×n LR-Mesh. The simulation is efficient in regards to size since both models use the same number of processors. The worst execution time for this simulation is O(n2) time, but we demonstrate that its average execution time is O(logn). The fastest existing simulation takes O(logn) time, but it uses an extremely large number of processors. On the other hand, the most efficient simulation in terms of size takes O(log2n) time with O(n4/log2n) processors. Both of the existing simulations are for the unrestricted DR-Mesh. This paper provides an important step towards efficiently simulating the unrestricted DR-Mesh on weaker models such as the R-Mesh and the LR-Mesh. [ABSTRACT FROM AUTHOR]