A new coupling solution for G3-PLC employment in MV smart grids

This paper proposes a new coupling solution for transmitting narrowband multicarrier power line communication (PLC) signals over medium voltage (MV) power lines. The proposed system is based on an innovative PLC coupling principle, patented by the authors, which exploits the capacitive divider embedded in voltage detecting systems (VDS) already installed inside the MV switchboard. Thus, no dedicated couplers have to be installed and no switchboard modifications or energy interruptions are needed. This allows a significant cost reduction of MV PLC implementation. A first prototype of the proposed coupling system was presented in previous papers: it had a 15 kHz bandwidth useful to couple single carrier PSK modulated PLC signals with a center frequency from 50–200 kHz. In this paper, a new prototype is developed with a larger bandwidth, up to 164 kHz, thus allowing to couple multicarrier G3-PLC signals using orthogonal frequency division multiplexing (OFDM) digital modulation. This modulation ensures a more robust communication even in harsh power line channels. In the paper, the new coupling system design is described in detail. A new procedure is presented for tuning the coupling system parameters at first installation in a generic MV switchboard. Finally, laboratory and in-field experimental test results are reported and discussed. The coupling performances are evaluated measuring the throughput and success rate in the case of both 18 and 36 subcarriers, in one of the different tone masks standardized for the FCC-above CENELEC band (that is, from 154.6875–487.5 kHz). The experimental results show an efficient behavior of the proposed coupler allowing a two-way communication of G3-PLC OFDM signals on MV networks

Modeling of tuning of microresonator filters by perturbational evaluation of cavity mode phase shifts

Microresonator filters, realized by evanescent coupling of circular cavities with two parallel bus waveguides, are promising candidates for applications in dense wavelength division multiplexing. Tunability of these filters is an essential feature for their successful deployment. In this paper we present a framework for modeling of tuning of the microresonators by changes of their cavity core refractive index. Using a reciprocity theorem, a perturbational expression for changes in the cavity propagation constants due to slight modifications of the cavity core refractive index is derived. This expression permits to analytically calculate shifts in spectral response of the 2D resonators. Comparisons of the resultant shifts and spectra with direct simulations based on coupled mode theory show satisfactory agreement

Design of a silicon cochlea system with biologically faithful response

This paper presents the design and simulation results of a silicon cochlea system that has closely similar behavior as the real cochlea. A cochlea filter-bank based on the improved three-stage filter cascade structure is used to model the frequency decomposition function of the basilar membrane; a filter tuning block is designed to model the adaptive response of the cochlea; besides, an asynchronous event-triggered spike codec is employed as the system interface with bank-end spiking neural networks. As shown in the simulation results, the system has biologically faithful frequency response, impulse response, and active adaptation behavior; also the system outputs multiple band-pass channels of spikes from which the original sound input can be recovered. The proposed silicon cochlea is feasible for analog VLSI implementation so that it not only emulates the way that sounds are preprocessed in human ears but also is able match the compact physical size of a real cochlea

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset

A Binaural Neuromorphic Auditory Sensor for FPGA: A Spike Signal Processing Approach

This paper presents a new architecture, design flow, and field-programmable gate array (FPGA) implementation analysis of a neuromorphic binaural auditory sensor, designed completely in the spike domain. Unlike digital cochleae that decompose audio signals using classical digital signal processing techniques, the model presented in this paper processes information directly encoded as spikes using pulse frequency modulation and provides a set of frequency-decomposed audio information using an address-event representation interface. In this case, a systematic approach to design led to a generic process for building, tuning, and implementing audio frequency decomposers with different features, facilitating synthesis with custom features. This allows researchers to implement their own parameterized neuromorphic auditory systems in a low-cost FPGA in order to study the audio processing and learning activity that takes place in the brain. In this paper, we present a 64-channel binaural neuromorphic auditory system implemented in a Virtex-5 FPGA using a commercial development board. The system was excited with a diverse set of audio signals in order to analyze its response and characterize its features. The neuromorphic auditory system response times and frequencies are reported. The experimental results of the proposed system implementation with 64-channel stereo are: a frequency range between 9.6 Hz and 14.6 kHz (adjustable), a maximum output event rate of 2.19 Mevents/s, a power consumption of 29.7 mW, the slices requirements of 11 141, and a system clock frequency of 27 MHz.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130