48,160 research outputs found
WHY IS NOT THERE A 20 KV ASYNCHRONOUS MOTOR?
The need for high-voltage asynchronous motors is relevant.
The most significant problem is the appearance of partial discharge.
Reliable methods for discharge parameter measurements have not been
fully developed yet. The measurement of partial discharge parameters is the
first step towards the realization of these high-voltage asynchronous motors.
The suggested measurement works in real time. The entire signal is first
cleaned, then tables on the intensity, duration and distance between impulses
of partial discharge are generated using a logical algorithm based on signal
shape recognition. The generated data contain all
the information about the partial discharge signal. Sorting of the obtained
data is performed in the DSP processor and only the interesting statistical
data are forwarded to the PC where the archiving or presentation of the results or
further processing are carried out. The AD converters,
CPLD and FPGA circuits almost of GHz speed
have a commercial price which means there is a possibility for a system which observes
shape of partial discharge impulses. The steps presented here constitute the
initial phase for the realization of a final system
Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review
A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance
Latency Optimized Asynchronous Early Output Ripple Carry Adder based on Delay-Insensitive Dual-Rail Data Encoding
Asynchronous circuits employing delay-insensitive codes for data
representation i.e. encoding and following a 4-phase return-to-zero protocol
for handshaking are generally robust. Depending upon whether a single
delay-insensitive code or multiple delay-insensitive code(s) are used for data
encoding, the encoding scheme is called homogeneous or heterogeneous
delay-insensitive data encoding. This article proposes a new latency optimized
early output asynchronous ripple carry adder (RCA) that utilizes single-bit
asynchronous full adders (SAFAs) and dual-bit asynchronous full adders (DAFAs)
which incorporate redundant logic and are based on the delay-insensitive
dual-rail code i.e. homogeneous data encoding, and follow a 4-phase
return-to-zero handshaking. Amongst various RCA, carry lookahead adder (CLA),
and carry select adder (CSLA) designs, which are based on homogeneous or
heterogeneous delay-insensitive data encodings which correspond to the
weak-indication or the early output timing model, the proposed early output
asynchronous RCA that incorporates SAFAs and DAFAs with redundant logic is
found to result in reduced latency for a dual-operand addition operation. In
particular, for a 32-bit asynchronous RCA, utilizing 15 stages of DAFAs and 2
stages of SAFAs leads to reduced latency. The theoretical worst-case latencies
of the different asynchronous adders were calculated by taking into account the
typical gate delays of a 32/28nm CMOS digital cell library, and a comparison is
made with their practical worst-case latencies estimated. The theoretical and
practical worst-case latencies show a close correlation....Comment: arXiv admin note: text overlap with arXiv:1704.0761
Towards a bio-inspired mixed-signal retinal processor
Published versio
Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data
In this work, two methods based on statistical models that take into account the temporal changes in the electroencephalographic (EEG) signal are proposed for asynchronous brain-computer interfaces (BCI) based on imaginary motor tasks. Unlike the current approaches to asynchronous BCI systems that make use of windowed versions of the EEG data combined with static classifiers, the methods proposed here are based on discriminative models that allow sequential labeling of data. In particular, the two methods we propose for asynchronous BCI are based on conditional random fields (CRFs) and latent dynamic CRFs (LDCRFs), respectively. We describe how the asynchronous BCI problem can be posed as a classification problem based on CRFs or LDCRFs, by defining appropriate random variables and their relationships. CRF allows modeling the extrinsic dynamics of data, making it possible to model the transitions between classes, which in this context correspond to distinct tasks in an asynchronous BCI system. On the other hand, LDCRF goes beyond this approach by incorporating latent variables that permit modeling the intrinsic structure for each class and at the same time allows modeling extrinsic dynamics. We apply our proposed methods on the publicly available BCI competition III dataset V as well as a data set recorded in our laboratory. Results obtained are compared to the top algorithm in the BCI competition as well as to methods based on hierarchical hidden Markov models (HHMMs), hierarchical hidden CRF (HHCRF), neural networks based on particle swarm optimization (IPSONN) and to a recently proposed approach based on neural networks and fuzzy theory, the S-dFasArt. Our experimental analysis demonstrates the improvements provided by our proposed methods in terms of classification accuracy
TensorLayer: A Versatile Library for Efficient Deep Learning Development
Deep learning has enabled major advances in the fields of computer vision,
natural language processing, and multimedia among many others. Developing a
deep learning system is arduous and complex, as it involves constructing neural
network architectures, managing training/trained models, tuning optimization
process, preprocessing and organizing data, etc. TensorLayer is a versatile
Python library that aims at helping researchers and engineers efficiently
develop deep learning systems. It offers rich abstractions for neural networks,
model and data management, and parallel workflow mechanism. While boosting
efficiency, TensorLayer maintains both performance and scalability. TensorLayer
was released in September 2016 on GitHub, and has helped people from academia
and industry develop real-world applications of deep learning.Comment: ACM Multimedia 201
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