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Luciano da F. Costa

Publications and source records attributed to Luciano da F. Costa.

At least 91 records · Page 5Linked to original sources

Characterizing Germanium Junction Transistors

Transistors have provided the basis of modern electronics. Being relatively intricate devices, and often exhibiting intense parameter variation, this type of electronic devices has motivated much research interest especially regarding their characterization and modeling. In this work, we apply a recently reported modeling methodology, based on the Early effect, for characterizing new old stock NPN and PNP small signal germanium junction transistors and comparing them to more modern silicon bipolar junction devices. The Early approach is special in the sense that its two parameters, namely the Early voltage $V_a$ and a proportionality parameter $s$, are fixed and independent of transistor operation. Remarkable results are obtained, including the fact that the four considered groups, namely NPN and PNP germanium and silicon devices, occupy mostly non-overlapping regions in the Early parameter space, with PNP devices presenting larger parameter variability in both cases. Surprisingly, the considered germanium devices exhibited smaller parameter variability than observed for the silicon counterparts. When mapped into the more traditional space defined by the current gain and output resistance parameters, the four transistor groups exhibited much larger overlaps and yielded a clustering structure much less organized than allowed by the Early mapping. This result suggest that the Early representation of transistors is more compatible and inherently related to the structure of amplifying devices such as those considered in this work. In addition, it was verified that the center of mass of each of the NPN-PNP pairs of germanium and silicon devices are crossed by respective $β$-isolines for gains of 130 and 250, respectively. Germanium devices were also characterized as having smaller output resistance and smaller magnitudes of Early voltage.

physics.app-ph↗

Characterizing Complementary Bipolar Junction Transistors by Early Modelling, Image Analysis, and Pattern Recognition

This work reports an approach to study complementary pairs of bipolar junction transistors, often used in push-pull circuits typically found at the output stages of operational amplifiers. After the data is acquired and pre-processed, an Early modeling approach is applied to estimate the two respective parameters (the Early voltage $V_a$ and the proportionality parameter $s$). A voting procedure, inspired on the Hough transform image analysis method, is adopted to improve the identification of $V_a$. Analytical relationships are derived between the traditional parameters current gain ($β$) and output resistance ($R_o$) and the two Early parameters. It is shown that $β$ tends to increase with $s$ for fixed $V_a$, while $R_o$ depends only on $V_a$, varying linearly with this parameter. Several interesting results are obtained with respect to 7 pairs of complementary BJTs, each represented by 10 samples. First, we have that the considered BJTs occupy a restricted band along the Early parameter space, and also that the NPN and PNP groups are mostly segregated into two respective regions in this space. In addition, PNP devices tended to present an intrinsically larger parameter variation. The NPN group tended to have higher $V_a$ magnitude and smaller $s$ than PNP devices. NPN transistors yielded comparable $β$ and larger $R_o$ than PNP transistors. A pattern recognition method was employed to obtain a linear separatrix between the NPN and PNP groups in the Early space and the respective average parameters were used to estimate respective prototype devices. Two complementary pairs of the real-world BJTs with large and small parameters differences were used in three configurations of push pull circuits, and the respective total harmonic distortions were measured and discussed, indicating a definite influence of parameter matching on the results.

physics.app-ph↗

How Integrated are Theoretical and Applied Physics?

How well integrated are more theoretically and application oriented works in Physics currently? This interesting question, which has several relevant implications, has been approached mostly in a more subjective way. Recent concepts and methods from network science are used in the current work in order to develop a more principled, quantitative and objective approach to gauging the integration and centrality of more theoretical/applied journals within the APS journals database, represented as a directed and undirected citation network. The results suggest a surprising level of integration between more theoretically and application oriented journals, which are also characterized by remarkably similar centralities in the network.

physics.soc-ph↗

Topology and Dynamics in Complex Networks: The Role of Edge Reciprocity

A key issue in complex systems regards the relationship between topology and dynamics. In this work, we use a recently introduced network property known as steering coefficient as a means to approach this issue with respect to different directed complex network systems under varying dynamics. Theoretical and real-world networks are considered, and the influences of reciprocity and average degree on the steering coefficient are quantified. A number of interesting results are reported that can assist the design of complex systems exhibiting larger or smaller relationships between topology and dynamics.

physics.soc-ph↗

Topological characterization of world cities

The topological organization of several world cities are studied according to respective representations by complex networks. As a first step, the city maps are processed by a recently developed methodology that allows the most significant urban region of each city to be identified. Then, we estimate many topological measures on the obtained networks, and apply multivariate statistics and data analysis methods to study and compare the topologies. Remarkably, the obtained results show that cities from specific continents, especially Anglo-Saxon America, tend to have particular topological properties. Such developments should contribute to better understanding how cities are organized and related to different geographical locations worldwide.

physics.soc-ph↗

An Image Analysis Approach to the Calligraphy of Books

Text network analysis has received increasing attention as a consequence of its wide range of applications. In this work, we extend a previous work founded on the study of topological features of mesoscopic networks. Here, the geometrical properties of visualized networks are quantified in terms of several image analysis techniques and used as subsidies for authorship attribution. It was found that the visual features account for performance similar to that achieved by using topological measurements. In addition, the combination of these two types of features improved the performance.

cs.CL↗

A Pattern Recognition Approach to Transistor Array Parameter Variance

The properties of bipolar junction transistors (BJTs) are known to vary in terms of their parameters. In this work, an experimental approach, including pattern recognition concepts and methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), was used to experimentally investigate the variation among BJTs belonging to integrated circuits known as transistor arrays. It was shown that a good deal of the devices variance can be captured using only two PCA axes. It was also verified that, though substantially small variation of parameters is observed for BJT from the same array, larger variation arises between BJTs from distinct arrays, suggesting the consideration of device characteristics in more critical analog designs. As a consequence of its supervised nature, LDA was able to provide a substantial separation of the BJT into clusters, corresponding to each transistor array. In addition, the LDA mapping into two dimensions revealed a clear relationship between the considered measurements. Interestingly, a specific mapping suggested by the PCA, involving the total harmonic distortion variation expressed in terms of the average voltage gain, yielded an even better separation between the transistor array clusters.

physics.ins-det↗

Connecting Network Science and Information Theory

A framework integrating information theory and network science is proposed, giving rise to a potentially new area. By incorporating and integrating concepts such as complexity, coding, topological projections and network dynamics, the proposed network-based framework paves the way not only to extending traditional information science, but also to modeling, characterizing and analyzing a broad class of real-world problems, from language communication to DNA coding. Basically, an original network is supposed to be transmitted, with or without compaction, through a sequence of symbols or time-series obtained by sampling its topology by some network dynamics, such as random walks. We show that the degree of compression is ultimately related to the ability to predict the frequency of symbols based on the topology of the original network and the adopted dynamics. The potential of the proposed approach is illustrated with respect to the efficiency of transmitting several types of topologies by using a variety of random walks. Several interesting results are obtained, including the behavior of the Barabási-Albert model oscillating between high and low performance depending on the considered dynamics, and the distinct performances obtained for two geographical models.

cs.IT↗

Knowledge Acquisition: A Complex Networks Approach

Complex networks have been found to provide a good representation of the structure of knowledge, as understood in terms of discoverable concepts and their relationships. In this context, the discovery process can be modeled as agents walking in a knowledge space. Recent studies proposed more realistic dynamics, including the possibility of agents being influenced by others with higher visibility or by their own memory. However, rather than dealing with these two concepts separately, as previously approached, in this study we propose a multi-agent random walk model for knowledge acquisition that incorporates both concepts. More specifically, we employed the true self avoiding walk alongside a new dynamics based on jumps, in which agents are attracted by the influence of others. That was achieved by using a Lévy flight influenced by a field of attraction emanating from the agents. In order to evaluate our approach, we use a set of network models and two real networks, one generated from Wikipedia and another from the Web of Science. The results were analyzed globally and by regions. In the global analysis, we found that most of the dynamics parameters do not significantly affect the discovery dynamics. The local analysis revealed a substantial difference of performance depending on the network regions where the dynamics are occurring. In particular, the dynamics at the core of networks tend to be more effective. The choice of the dynamics parameters also had no significant impact to the acquisition performance for the considered knowledge networks, even at the local scale.

cs.SI↗

Representation of texts as complex networks: a mesoscopic approach

Statistical techniques that analyze texts, referred to as text analytics, have departed from the use of simple word count statistics towards a new paradigm. Text mining now hinges on a more sophisticated set of methods, including the representations in terms of complex networks. While well-established word-adjacency (co-occurrence) methods successfully grasp syntactical features of written texts, they are unable to represent important aspects of textual data, such as its topical structure, i.e. the sequence of subjects developing at a mesoscopic level along the text. Such aspects are often overlooked by current methodologies. In order to grasp the mesoscopic characteristics of semantical content in written texts, we devised a network model which is able to analyze documents in a multi-scale fashion. In the proposed model, a limited amount of adjacent paragraphs are represented as nodes, which are connected whenever they share a minimum semantical content. To illustrate the capabilities of our model, we present, as a case example, a qualitative analysis of "Alice's Adventures in Wonderland". We show that the mesoscopic structure of a document, modeled as a network, reveals many semantic traits of texts. Such an approach paves the way to a myriad of semantic-based applications. In addition, our approach is illustrated in a machine learning context, in which texts are classified among real texts and randomized instances.

cs.CL↗

Clustering Algorithms: A Comparative Approach

Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While a myriad of classification methods have been proposed, there is no consensus on which methods are more suitable for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios. In this context, we performed a systematic comparison of 7 well-known clustering methods available in the R language. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties (number of classes, separation between classes, etc). In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach usually outperformed the other clustering algorithms. We also found that the default configuration of the adopted implementations was not accurate. In these cases, a simple approach based on random selection of parameters values proved to be a good alternative to improve the performance. All in all, the reported approach provides subsidies guiding the choice of clustering algorithms.

cs.LG↗

Rumor propagation with heterogeneous transmission in social networks

Rumor models consider that information transmission occurs with the same probability between each pair of nodes. However, this assumption is not observed in social networks, which contain influential spreaders. To overcome this limitation, we assume that central individuals have a higher capacity of convincing their neighbors than peripheral subjects. By extensive numerical simulations we find that the spreading is improved in scale-free networks when the transmission probability is proportional to PageRank, degree, and betweenness centrality. In addition, the results suggest that the spreading can be controlled by adjusting the transmission probabilities of the most central nodes. Our results provide a conceptual framework for understanding the interplay between rumor propagation and heterogeneous transmission in social networks.

physics.soc-ph↗

Negative Feedback, Linearity and Parameter Invariance in Linear Electronics

Negative feedback is a powerful approach capable of improving several aspects of a system. In linear electronics, it has been critical for allowing invariance to device properties. Negative feedback is also known to enhance linearity in amplification, which is one of the most important foundations of linear electronics. At the same time, thousands of transistors types have been made available, suggesting that these devices, in addition to their known variability of parameters, have distinguishing properties. The current work reports a systematic approach to quantifying the potential of negative feedback, with respect to bipolar transistors, as a means to providing device invariance and linearity. Several methods, including concepts from multivariate statistics and complex systems, are applied at the theoretical as well as experimental levels, and a number of interesting results are obtained and discussed. For instance, it has been verified that the transistors types indeed have well-defined characteristics which clearly segregate them into groups. The addition of feedback at moderate and intense levels promoted uniformization of the properties of these transistors when used in a class A common emitter configuration. However, such effect occurred with different efficiency regarding the considered device features, and even intense feedback was unable to completely eliminate device dependence. This indicates that it would be interesting to consider the device properties in linear design even when negative feedback is applied. We also verified that the linearization induced in the considered experiments is relatively modest, with effects that depend on type of transfer function of the original devices.

physics.ins-det↗

Patterns of authors contribution in scientific manuscripts

Science is becoming increasingly more interdisciplinary, giving rise to more diversity in the areas of expertise within research labs and groups. This also have brought changes to the role researchers in scientific works. As a consequence, multi-authored scientific papers have now became a norm for high quality research. Unfortunately, such a phenomenon induces bias to existing metrics employed to evaluate the productivity and success of researchers. While some metrics were adapted to account for the rank of authors in a paper, many journals are now requiring a description of the specific roles of each author in a publication. Surprisingly, the investigation of the relationship between the rank of authors and their contributions has been limited to a few studies. By analyzing such kind of data, here we show, quantitatively, that the regularity in the authorship contributions decreases with the number of authors in a paper. Furthermore, we found that the rank of authors and their roles in papers follows three general patterns according to the nature of their contributions, such as writing, data analysis, and the conduction of experiments. This was accomplished by collecting and analyzing the data retrieved from PLoS ONE and by devising an entropy-based measurement to quantify the effective number of authors in a paper according to their contributions. The analysis of such patterns confirms that some aspects of the author ranking are in accordance with the expected convention, such as the fact that the first and last authors are more likely to contribute more in a scientific work. Conversely, such analysis also revealed that authors in the intermediary positions of the rank contribute more in certain specific roles, such as the task of collecting data. This indicates that the an unbiased evaluation of researchers must take into account the distinct types of scientific contributions.

cs.DL↗

Seeking Maximum Linearity of Transfer Functions

Linearity is an important and frequently sought property in electronics and instrumentation. Here, we report a method capable of, given a transfer function, identifying the respective most linear region of operation with a fixed width. This methodology, which is based on least squares regression and systematic consideration of all possible regions, has been illustrated with respect to both an analytical (sigmoid transfer function) and real-world (low-power, one-stage class A transistor amplifier) situations. In the former case, the method was found to identity the theoretically optimal region of operation even in presence of noise. In the latter case, it was possible to identify an amplifier circuit configuration providing a good compromise between linearity, amplification and output resistance. The transistor amplifier application, which was addressed in terms of transfer functions derived from its experimentally obtained characteristic surface, also yielded contributions such as the estimation of local constants of the device, as opposed to typically considered average values. Moreover, the obtained characteristic surfaces of the considered transistor (a generic, low signal device) revealed a surprisingly complex structure. The reported method and results paves the way to several other applications in other types of devices and systems, intelligent control operation, and other areas such as identifying regions of power law behavior.

physics.data-an↗

A framework for evaluating complex networks measurements

A good deal of current research in complex networks involves the characterization and/or classification of the topological properties of given structures, which has motivated several respective measurements. This letter proposes a framework for evaluating the quality of complex network measurements in terms of their effective resolution, degree of degeneracy and discriminability. The potential of the suggested approach is illustrated with respect to comparing the characterization of several model and real-world networks by using concentric and symmetry measurements. The results indicate a markedly superior performance for the latter type of mapping.

physics.soc-ph↗

The relationship between structure and function in locally observed complex networks

Recently, some studies started to unveil the wealthy of interactions that occur between groups of nodes when looking at the small scale of interactions taking place in complex networks. Such findings claim for a new systematic methodology to quantify, at node level, how a dynamics is being influenced (or differentiated) by the structure of the underlying system. Here we define a new measure that, based on dynamical characteristics obtained for a large set of initial conditions, compares the dynamical behavior of the nodes present in the system. Through this measure we find that the geographic and Barabási-Albert models have high capacity for generating networks that exhibit groups of nodes with distinct dynamics compared to the rest of the network. The application of our methodology is illustrated with respect of two real systems. In the first we use the neuronal network of the nematode Caenorhabditis elegans to show that the interneurons of the ventral cord of the nematode presents a very large dynamical differentiation when compared to the rest of the network. The second application concerns the SIS epidemic model on an airport network, where we quantify how different the distribution of infection times of high and low degree nodes can be, when compared to the expected value for the network.

physics.soc-ph↗

Random walks in directed modular networks

Because diffusion typically involves symmetric interactions, scant attention has been focused on studying asymmetric cases. However, important networked systems underlain by diffusion (e.g. cortical networks and WWW) are inherently directed. In the case of undirected diffusion, it can be shown that the steady-state probability of the random walk dynamics is fully correlated with the degree, which no longer holds for directed networks. We investigate the relationship between such probability and the inward node degree, which we call efficiency, in modular networks. Our findings show that the efficiency of a given community depends mostly on the balance between its ingoing and outgoing connections. In addition, we derive analytical expressions to show that the internal degree of the nodes do not play a crucial role in their efficiency, when considering the Erdős-Rényi and Barabási-Albert models. The results are illustrated with respect to the macaque cortical network, providing subsidies for improving transportation and communication systems.

cond-mat.stat-mech↗