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The present work describes the Design and elaboration of a prototype that by means of an algorithm links with a mobile application (App) and allows the orientation of visually impaired people implemented in the Faculty of Computer Science of the Technical University of Manabí; it consists of two parts, the mobile application which was developed in the programming environment Android Studio that allows detecting wireless signals (standard IEEE 802.11); the device detects certain pre-programmed signals at a certain distance will emit an audio that will be accompanied by a different vibration for each reference point indicating the sector in which it is circulating. The other part of the prototype are the reference points or access points, whose main component is a Wireless module (NodeMcu V3 LoLin), which through an algorithm emits signaling in the ISM band of 2.4 GHZ to mobile terminals. The reference points have energy autonomy of up to 20 hours from the alternating current supply by means of a photovoltaic panel in an automatic switching circuit. [ABSTRACT FROM AUTHOR]

Modern multicore embedded systems often execute applications that rely heavily on concurrent data structures. The selection of efficient concurrent data structure implementations for a specific application is usually a complex and time consuming task, because each design decision often affects the performance and the energy consumption of the embedded system in various and occasionally unpredictable ways. The complexity is normally addressed by developers by adopting ad-hoc design solutions, which are often suboptimal and yield poor results. To face this problem, we propose a semi-automated methodology for the optimization of applications that utilize concurrent data structures that is based on design space exploration. The proposed approach is evaluated by using both microbenchmarks and real-world applications that are executed on multicore embedded systems with different architectural specifications. Our results show that we can identify various trade-offs between different data structure implementations that can be used to optimize applications that rely on concurrent data structures. [ABSTRACT FROM PUBLISHER]

Packet classification is an enabling technology for various network services. Fast single-match packet classification can be achieved by using ternary content addressable memory (TCAM) because of the superior speed performance. TCAM has some drawbacks including incapability to store arbitrary ranges, confined TCAM capacity and limited choices of entry lengths. Moreover, TCAM only reports the first matching entry to impose a limitation on supporting multi-match packet classification, which requires all matching rules. The existing algorithms deal with the issues of TCAM-based multi-match packet classification by burdening TCAM with extra entries and/or accesses. In this work, we offload the overhead of TCAM to static random access memory (SRAM) to achieve efficient multi-match packet classification. Our scheme synthesizes TCAM compatible entries by using binary decision trees and employs SRAM for further comparisons. Each synthesized entry can be stored in one TCAM entry to significantly reduce TCAM consumption and fulfill low power consumption. The experimental results show that our scheme can lower the demand of TCAM to improve both search latency and energy efficiency. The scalability of TCAM-based multi-match packet classification can thus be improved drastically. [ABSTRACT FROM PUBLISHER]

This article presents differential relaying and condition monitoring of power transformer using minimal radial basis function neural network (MRBFNN). This type of RBF neural network uses a sequential learning procedure to determine the optimum number of neurons in the hidden layer without resorting to trial and error. This ANN-based scheme monitors operating conditions of the transformer and detects the fault and issues the trip signal in case of an internal fault. It has been realized through two different ANN structures using the minimal radial basis function (MRBF) learning algorithm. The proposed protection scheme has been evaluated using simulated data obtained through EMTP/ATP package. The results amply demonstrate the capabilities of the proposed fault detector (FD) and condition monitor (CM) in terms of accuracy and speed with respect to detection of fault, classification and pattern recognition of different events of power transformer. The number of training patterns and training time are drastically reduced and significant accuracy is achieved. [ABSTRACT FROM AUTHOR]