Design of an optimal ANFIS traffic signal controller by using cuckoo search for an isolated intersection

An optimal design of Adaptive Neuro-Fuzzy Inference System (ANFIS) traffic signal controller is presented in this paper. The proposed controller aims to adjust a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. The ANFIS controller is trained, to learned how to set green times for each traffic phase. This intelligent controller uses the Cuckoo Search (CS) algorithm to tune its parameters during the learning pried. Evaluating the performance of the proposed controller in comparison with the performance of a FLS controller (FLC) with predefined rules and membership functions, and also three fixed-Time controllers, illustrates the better performance of the optimal ANFIS controller against the other benchmark controllers.

EEG signal analysis for BCI application using fuzzy system

An approach to EEG signal classification for brain-computer interface (BCI) application using fuzzy standard additive model is introduced in this paper. The Wilcoxon test is employed to rank wavelet coefficients. Top ranking wavelets are used to form a feature set that serves as inputs to the fuzzy classifiers. Experiments are carried out using two benchmark datasets, Ia and Ib, downloaded from the BCI competition II. Prevalent classifiers including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system are also implemented for comparisons. Experimental results show the dominance of the proposed method against competing approaches.

Computational intelligence-based traffic signal timing optimization

 Traffic congestion has explicit effects on productivity and efficiency, as well as side effects on environmental sustainability and health. Controlling traffic flows at intersections is recognized as a beneficial technique, to decrease daily travel times. This thesis applies computational intelligence to optimize traffic signals' timing and reduce urban traffic.

Design of distributed adaptive neural traffic signal timing controller by cuckoo search optimization

This paper focuses on designing an adaptive controller for controlling traffic signal timing. Urban traffic is an inevitable part in modern cities and traffic signal controllers are effective tools to control it. In this regard, this paper proposes a distributed neural network (NN) controller for traffic signal timing. This controller applies cuckoo search (CS) optimization methods to find the optimal parameters in design of an adaptive traffic signal timing control system. The evaluation of the performance of the designed controller is done in a multi-intersection traffic network. The developed controller shows a promising improvement in reducing travel delay time compared to traditional fixed-time control systems.

Mixed-signal analog-digital circuits design on the pre-diffused digital array using trapezoidal association of transistors

The mixed-signal and analog design on a pre-diffused array is a challenging task, given that the digital array is a linear matrix arrangement of minimum-length transistors. To surmount this drawback a specific discipline for designing analog circuits over such array is required. An important novel technique proposed is the use of TAT (Trapezoidal Associations of Transistors) composite transistors on the semi-custom Sea-Of-Transistors (SOT) array. The analysis and advantages of TAT arrangement are extensively analyzed and demonstrated, with simulation and measurement comparisons to equivalent single transistors. Basic analog cells were also designed as well in full-custom and TAT versions in 1.0mm and 0.5mm digital CMOS technologies. Most of the circuits were prototyped in full-custom and TAT-based on pre-diffused SOT arrays. An innovative demonstration of the TAT technique is shown with the design and implementation of a mixed-signal analog system, i. e., a fully differential 2nd order Sigma-Delta Analog-to-Digital (A/D) modulator, fabricated in both full-custom and SOT array methodologies in 0.5mm CMOS technology from MOSIS foundry. Three test-chips were designed and fabricated in 0.5mm. Two of them are IC chips containing the full-custom and SOT array versions of a 2nd-Order Sigma-Delta A/D modulator. The third IC contains a transistors-structure (TAT and single) and analog cells placed side-by-side, block components (Comparator and Folded-cascode OTA) of the Sigma-Delta modulator.

Do dividends signal more earnings?

Signaling models have contributed to the corporate finance literature by formalizing "the informational content of dividends" hypothesis. However, these models are under criticism of empirical literature, as weak evidences were found supporting one of the main predicitions: the positive relation between changes in dividends and changes in earnings. We claim thaht the failure to verify this prediction does not invalidate the signaling approach. The models developed up to now assume or derive utility functions with the single-crossing property. We show thaht signaling is possible in the absence of this property and, in this case, changes in dividend and changes in earnings can be positively or negatively related.

A Modular and digitally programmable interface based on band-pass sigma-delta modulator for mixed-signal systems-on-chip

The focus of this thesis is to discuss the development and modeling of an interface architecture to be employed for interfacing analog signals in mixed-signal SOC. We claim that the approach that is going to be presented is able to achieve wide frequency range, and covers a large range of applications with constant performance, allied to digital configuration compatibility. Our primary assumptions are to use a fixed analog block and to promote application configurability in the digital domain, which leads to a mixed-signal interface. The use of a fixed analog block avoids the performance loss common to configurable analog blocks. The usage of configurability on the digital domain makes possible the use of all existing tools for high level design, simulation and synthesis to implement the target application, with very good performance prediction. The proposed approach utilizes the concept of frequency translation (mixing) of the input signal followed by its conversion to the ΣΔ domain, which makes possible the use of a fairly constant analog block, and also, a uniform treatment of input signal from DC to high frequencies. The programmability is performed in the ΣΔ digital domain where performance can be closely achieved according to application specification. The interface performance theoretical and simulation model are developed for design space exploration and for physical design support. Two prototypes are built and characterized to validate the proposed model and to implement some application examples. The usage of this interface as a multi-band parametric ADC and as a two channels analog multiplier and adder are shown. The multi-channel analog interface architecture is also presented. The characterization measurements support the main advantages of the approach proposed.

Do dividends signal more earnings? a theoretical analysis

The signaling models have contributed to the literature of corporate finance by the formalization of "the informational content of dividends hypothesis". However, these models are under criticism of empirical works, as weak evidences were found supporting one of the main predictions: the positive relation between changes in dividends and changes in earnings. We claim that the failure to verify this prediction does not invalidate the signaling approach. The mo deIs developed up to now assume or derive utility functions with the single-crossing property. We show that signaling is possible in the absence of this property and, in this case, changes in dividend and changes in earnings can be positively or negatively related.

Aluminum tolerance on genotypes of signal grass

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Signal transduction-related responses to phytohormones and environmental challenges in sugarcane

Background: Sugarcane is an increasingly economically and environmentally important C4 grass, used for the production of sugar and bioethanol, a low-carbon emission fuel. Sugarcane originated from crosses of Saccharum species and is noted for its unique capacity to accumulate high amounts of sucrose in its stems. Environmental stresses limit enormously sugarcane productivity worldwide. To investigate transcriptome changes in response to environmental inputs that alter yield we used cDNA microarrays to profile expression of 1,545 genes in plants submitted to drought, phosphate starvation, herbivory and N-2-fixing endophytic bacteria. We also investigated the response to phytohormones (abscisic acid and methyl jasmonate). The arrayed elements correspond mostly to genes involved in signal transduction, hormone biosynthesis, transcription factors, novel genes and genes corresponding to unknown proteins.Results: Adopting an outliers searching method 179 genes with strikingly different expression levels were identified as differentially expressed in at least one of the treatments analysed. Self Organizing Maps were used to cluster the expression profiles of 695 genes that showed a highly correlated expression pattern among replicates. The expression data for 22 genes was evaluated for 36 experimental data points by quantitative RT-PCR indicating a validation rate of 80.5% using three biological experimental replicates. The SUCAST Database was created that provides public access to the data described in this work, linked to tissue expression profiling and the SUCAST gene category and sequence analysis. The SUCAST database also includes a categorization of the sugarcane kinome based on a phylogenetic grouping that included 182 undefined kinases.Conclusion: An extensive study on the sugarcane transcriptome was performed. Sugarcane genes responsive to phytohormones and to challenges sugarcane commonly deals with in the field were identified. Additionally, the protein kinases were annotated based on a phylogenetic approach. The experimental design and statistical analysis applied proved robust to unravel genes associated with a diverse array of conditions attributing novel functions to previously unknown or undefined genes. The data consolidated in the SUCAST database resource can guide further studies and be useful for the development of improved sugarcane varieties.

Transcription profiling of signal transduction-related genes in sugarcane tissues

A collection of 237,954 sugarcane ESTs was examined in search of signal transduction genes. Over 3,500 components involved in several aspects of signal transduction, transcription, development, cell cycle, stress responses and pathogen interaction were compiled into the Sugarcane Signal Transduction (SUCAST) Catalogue. Sequence comparisons and protein domain analysis revealed 477 receptors, 510 protein kinases, 107 protein phosphatases, 75 small GTPases, 17 G-proteins, 114 calcium and inositol metabolism proteins, and over 600 transcription factors. The elements were distributed into 29 main categories subdivided into 409 sub-categories. Genes with no matches in the public databases and of unknown function were also catalogued. A cDNA microarray was constructed to profile individual variation of plants cultivated in the field and transcript abundance in six plant organs (flowers, roots, leaves, lateral buds, and 1(st) and 4(th) internodes). From 1280 distinct elements analyzed, 217 (17%) presented differential expression in two biological samples of at least one of the tissues tested. A total of 153 genes (12%) presented highly similar expression levels in all tissues. A virtual profile matrix was constructed and the expression profiles were validated by real-time PCR. The expression data presented can aid in assigning function for the sugarcane genes and be useful for promoter characterization of this and other economically important grasses.

Signal transducer and activator of transcription 3-regulated sarcoendoplasmic reticulurn Ca2+-ATPase 2 expression by prolactin and glucocorticoids is involved in the adaptation of insulin secretory response during the peripartum period

During pregnancy, the maternal endocrine pancreas undergoes, as a consequence of placental lactogens and prolactin (PR,L) action, functional changes that are characterized by increased glucose-induced insulin secretion. After delivery, the maternal endocrine pancreas rapidly returns to nonpregnant state, which is mainly attributed to the increased serum levels of glucocorticoids (GCs). Although GCs are known to decrease insulin secretion and counteract PRL action, the mechanisms for these effects are poorly understood. We have previously demonstrated that signal transducer and activator of transcription 3 (STAT3) is increased in islets treated with PRL. In the present study, we show that STAT3 expression and serine phosphorylation are increased in pancreatic islets at the end of pregnancy (P19). STAT3 serine phosphorylation rapidly returned to basal levels 3 days after delivery (U). The expression of the sarcoendoplasmic reticulum Ca2+-ATPase 2 (SERCA2), a crucial protein involved in the regulation of calcium handling in P-cells, was also increased in P19, returning to basal levels at L3. PRL increased SERCA2 and STAT3 expressions and STAT3 serine phosphorylation in RINm5F cells. The upregulation of SERCA2 by PRL was abolished after STAT3 knockdown. Moreover, PRL-induced STAT3 serine phosphorylation and SERCA2 expression were inhibited by dexamethasone (DEX). Insulin secretion from islets of PI 9 rats pre-incubated with thapsigargin and L3 rats showed a dramatic suppression of first phase of insulin release. The present results indicate that PRL regulates SERCA2 expression by a STAT3-dependent mechanism. PRL effect is counteracted by DEX and might contribute to the adaptation of maternal endocrine pancreas during the peripartum period.

Indicadores de cálcio e de voltagem codificados geneticamente na detecção de potenciais de ação e inputs sinápticos em cultura de neurônios hipocampais

Recently, genetically encoded optical indicators have emerged as noninvasive tools of high spatial and temporal resolution utilized to monitor the activity of individual neurons and specific neuronal populations. The increasing number of new optogenetic indicators, together with the absence of comparisons under identical conditions, has generated difficulty in choosing the most appropriate protein, depending on the experimental design. Therefore, the purpose of our study was to compare three recently developed reporter proteins: the calcium indicators GCaMP3 and R-GECO1, and the voltage indicator VSFP butterfly1.2. These probes were expressed in hippocampal neurons in culture, which were subjected to patchclamp recordings and optical imaging. The three groups (each one expressing a protein) exhibited similar values of membrane potential (in mV, GCaMP3: -56 ±8.0, R-GECO1: -57 ±2.5; VSFP: -60 ±3.9, p = 0.86); however, the group of neurons expressing VSFP showed a lower average of input resistance than the other groups (in Mohms, GCaMP3: 161 ±18.3; GECO1-R: 128 ±15.3; VSFP: 94 ±14.0, p = 0.02). Each neuron was submitted to current injections at different frequencies (10 Hz, 5 Hz, 3 Hz, 1.5 Hz, and 0.7 Hz) and their fluorescence responses were recorded in time. In our study, only 26.7% (4/15) of the neurons expressing VSFP showed detectable fluorescence signal in response to action potentials (APs). The average signal-to-noise ratio (SNR) obtained in response to five spikes (at 10 Hz) was small (1.3 ± 0.21), however the rapid kinetics of the VSFP allowed discrimination of APs as individual peaks, with detection of 53% of the evoked APs. Frequencies below 5 Hz and subthreshold signals were undetectable due to high noise. On the other hand, calcium indicators showed the greatest change in fluorescence following the same protocol (five APs at 10 Hz). Among the GCaMP3 expressing neurons, 80% (8/10) exhibited signal, with an average SNR value of 21 ±6.69 (soma), while for the R-GECO1 neurons, 50% (2/4) of the neurons had signal, with a mean SNR value of 52 ±19.7 (soma). For protocols at 10 Hz, 54% of the evoked APs were detected with GCaMP3 and 85% with R-GECO1. APs were detectable in all the analyzed frequencies and fluorescence signals were detected from subthreshold depolarizations as well. Because GCaMP3 is the most likely to yield fluorescence signal and with high SNR, some experiments were performed only with this probe. We demonstrate that GCaMP3 is effective in detecting synaptic inputs (involving Ca2+ influx), with high spatial and temporal resolution. Differences were also observed between the SNR values resulting from evoked APs, compared to spontaneous APs. In recordings of groups of cells, GCaMP3 showed clear discrimination between activated and silent cells, and reveals itself as a potential tool in studies of neuronal synchronization. Thus, our results indicate that the presently available calcium indicators allow detailed studies on neuronal communication, ranging from individual dendritic spines to the investigation of events of synchrony in neuronal networks genetically defined. In contrast, studies employing VSFPs represent a promising technology for monitoring neural activity and, although still to be improved, they may become more appropriate than calcium indicators, since neurons work on a time scale faster than events of calcium may foresee

Signal-flow graphs: Direct method of reduction and MATLAB implementation

Block diagrams and signal-flow graphs are used to represent and to obtain the transfer function of interconnected systems. The reduction of signal-flow graphs is considered simpler than the reduction of block diagrams for systems with complex interrelationships. Signal-flow graphs reduction can be made without graphic manipulations of diagrams, and it is attractive for a computational implementation. In this paper the authors propose a computational method for direct reduction of signal-flow graphs. This method uses results presented in this paper about the calculation of literal determinants without symbolic mathematics tools. The Cramer's rule is applied for the solution of a set of linear equations, A program in MATLAB language for reduction of signal-flow graphs with the proposed method is presented.