Herón Molina-Lozano

A method for environmental acoustic analysis improvement based on individual evaluation of common sources in urban areas

Noise levels of common sources such as vehicles, whistles, sirens, car horns and crowd sounds are mixed in urban soundscapes. Nowadays, environmental acoustic analysis is performed based on mixture signals recorded by monitoring systems. These mixed signals make it difficult for individual analysis which is useful in taking actions to reduce and control environmental noise. This paper aims at separating, individually, the noise source from recorded mixtures in order to evaluate the noise level of each estimated source. A method based on blind deconvolution and blind source separation in the wavelet domain is proposed. This approach provides a basis to improve results obtained in monitoring and analysis of common noise sources in urban areas. The method validation is through experiments based on knowledge of the predominant noise sources in urban soundscapes. Actual recordings of common noise sources are used to acquire mixture signals using a microphone array in semi-controlled environments. The developed method has demonstrated great performance improvements in identification, analysis and evaluation of common urban sources.

A new fast fuzzy Cocke–Younger–Kasami algorithm for DNA strings analysis

In this paper we present a variation of the Cocke–Younger–Kasami algorithm (CYK algorithm) for the analysis of fuzzy free context languages applied to DNA strings. We propose a variation of the original CYK algorithm where we prove that the computational order of the new CYK algorithm is O(n). We prove that the new algorithm only uses O(2n) memory locations. The fuzzy context-free grammar (FCFG) is obtained from the DNA. The algorithm can be used to find regulatory motifs among other applications. In order to demonstrate the applications of the proposed algorithm, we present two examples. In the first example, we prove that it is possible to define a fuzzy grammar for a prototype DNA sequence and then find the membership grade of any arbitrary sequence against this specific pattern. As a second example, we construct a fuzzy grammar from the alignment of promoters obtained by a logo sequence algorithm for the Escherichia coli K12 DNA string, and then show how the proposed method can be used for discovery of the regulatory motifs.

A Feasible Genetic Optimization Strategy for Parametric Interval Type-2 Fuzzy Logic Systems

This paper presents an optimization strategy for interval type-2 fuzzy systems by using the conjunction operation called the (p)-monotone sum of t-norms. A direct-current servomotor control system is implemented to test the performance of the type-1, interval type-2 and interval type-2 fuzzy systems with parametric operations, under several noisy conditions. To rate them, a multi-objective fitness function, based on the main transient parameters, is proposed to ensure the genetic algorithm to find the best squared feedback signal, when a white noise signal with different amplitudes is added to the reference. In addition, the optimization strategy includes the parametric conjunction suppression to analyze how a rule-associated parametric conjunction directly influences on system performance. Such rule suppression can be used to reduce the number of parametric conjunction operations required to obtain an additional performance improvement. Experimental results of the servomotor control system show that parametric conjunctions used in the interval type-2 fuzzy logic system provide additional advantages over its nonparametric counterpart.

Performance Analysis of Cluster Formation in Wireless Sensor Networks

Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN) use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes.

Hardware Design of Digital Parametric Conjunctors and t-Norms

Abstract This paper presents the hardware design and its implementation on FPGA of several parametric families of digital conjunctors and t-norms built from simple basic t-norms. The authors propose the method of unified presentation of the p-monotone sum, the simplified versions of the ordinal sum of t-norms and t-subnorms, and the method of extension of t-norms by the drastic t-norm. Such unification gives possibility to join several methods of construction of parametric digital conjunctors and t-norms in one scheme with the efficient FPGA implementation. The logic schemes of the proposed design are presented, and the comparative analysis of the latency time and the resources used for the implementation is given.

The Tellez-Molina-Villa algorithm

The Tellez-Molina-Villa (TMV) algorithm is a new defuzzification method for interval type-2 fuzzy systems. It is based on found the mean trajectory of any interval type-2 fuzzy set. With the mean trajectory we pretend to find the type-1 reduced fuzzy set of the interval type-2 fuzzy set. With this algorithm we try to find the generalized centroid of any interval type-2 fuzzy set. Also, we try to increase the type-2 fuzzy logic system accuracy. In general we found from 5 defuzzification methods that try to extract a crisp value from an interval type-2 fuzzy set as a representative value. First is necessary to obtain a type-1 fuzzy set from the type-2 fuzzy set, second from this reduced fuzzy set obtain a single crisp value. This crisp value represents lot of information, so that is necessary to do these steps carefully because we can obtain misinformation from the type-2 fuzzy inference system. In this paper we present some result from the new algorithm, and in order to compare the TMV algorithm we present comparative results with 5 type-2 defuzzification methods. From the obtained results we demonstrated that the TMV approach performs better that the Nie-Tan method. In fact, we can say that the TMV algorithm has at least equivalent results than Karnik-Mendel algorithm that in our opinion is one of the best defuzzification methods, but with the difference that the TMV algorithm is based on the mean trajectory of an interval type-2 fuzz set.

A compact CMOS Class-AB analog median filter

A power efficient implementation of a CMOS Class-AB analog median filter is presented. The median detector is based on transconductance comparators accomplished with new Differential Flipped Voltage Followers. The followers employ a current comparator to switch-on an auxiliary transistor to drive additional current whenever it is required, performing Class-AB operation. Area is saved by taking advantage of the large impedance node of the current comparator to accomplish Miller frequency compensation. Simulation results using the ON-SEMI 0.5 μm technology parameters validate the proposed structure.

Optimal Transmission Thresholds for QoS Guarantees in WSNs

In Wireless Sensor Networks (WSNs), each node typically transmits several control and data packets in a contention fashion to the sink. In this work, we mathematically analyze and study three unscheduled transmission schemes for control packets in a cluster-based architecture named Fixed Scheme (FS), Adaptive by Estimation Scheme (AES) and Adaptive by Gamma Scheme (AGS), in order to offer QoS guarantees in terms of system lifetime (related to energy consumption) and reporting delay (related to cluster formation delay). In the literature, different adaptive schemes have been proposed, and also there is research about the appropriate value selection of the transmission probability for the cluster formation. However, it largely overlooked the minimum and maximum values for the transmission probability that entails the best performance. Based on the numerical results, we show that the threshold values are just as important in the system design as the actual value of the transmission probability in adaptive schemes (AES and AGS), to achieve QoS guarantees.

An Automatic Functional Coverage for Digital Systems Through a Binary Particle Swarm Optimization Algorithm with a Reinitialization Mechanism

At present, functional verification represents the most expensive part of the digital systems design. Moreover, different problems such as: clock synchronization, code compatibility, simulation automation, new design methodologies, proper use of coverage metrics, among others represent challenges in this area. The automated test vector generation is involved in these problems. In this work, an automated functional test sequences generation for digital systems based on the use of coverage models and a binary Particle Swarm Optimization algorithm with a reinitialization mechanism (BPSOr) is described. Also, a comparison with other meta-heuristic algorithms such as: Genetic algorithms (GA) and pseudo-random generation is presented using different fitness functions, coverage models and devices under verification. The main strategy is based on the combination of the simulation and meta-heuristic algorithms to test the device behavior through the generation of test vector sequences. According to the results, the proposed test generation method represents a good alternative to increase the functional coverage during the automated functional verification of block-level digital systems verification.

A 90 µm × 64 µm 225 µW class-AB CMOS differential flipped voltage follower with output driving capability up to 100 pF

A compact differential flipped voltage follower (DFVF) with low power consumption, capable to deliver currents several orders of magnitude larger than its quiescent current and with large capacitive loads is presented. In the proposed circuit, a current comparator activates an auxiliary transistor whenever is required to hand over additional current and reach class-AB operation. Furthermore, Miller compensation is performed, by taking advantage of the large impedance node of the comparator it is possible to reduce forty times the compensation capacitor compared to other topologies under the same conditions. The proposed architecture is validated by post-layout simulations using the parameters of an ON SEMI, double-poly, three metal layers, 0.5@mm CMOS technology and the Pelgroms mismatch model. A Winner-Takes-All circuit, a median filter and a current conveyor are presented as examples of application of the proposed topology.