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6th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Savulescu, Corina Iorgulescu, Mariana Univ Pitesti Elect Comp Commun & Elect Engn Dept Pitesti Romania

ISBN: (纸本)9781479954797

The aim of this paper is to propose a reliable and efficient solution for detecting and correcting errors in communication channels. The implementation method of Hamming decoder is the first error correction code used in computers. We used a Spartan 3 reconfigurable circuit because this board is fully compatible with all versions of the Xilinx ISE tools, including the free WebPack. We studied linear and cyclic codes to demonstrate that its manner of implementation can be easily adapted to any structure code. For the theoretical part of this paper, we reviewed general rules used in Hamming codes' implementation. In order to create a testing platform we adapted the presented rules to be hardcoded into a Spartan 3 development board. Subsequently we presented the blueprints of our Hamming's linear decoder development. The results of our experiments revealed the benefits of using the Hamming codes for linear decoders onto a reconfigurable platform. In conclusion, our solution allows the recognition of one wrong symbol from a code sequence using either linear or cyclic Hamming codes.

关键词： code reconfigurable hardware Hamming codes linear decoder

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Low computational complexity methods for decoding of STBC in the uplink of a massive MIMO system

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2020年第1期2020卷 1-17页

作者： Mousavi, Seyed Hosein Pourrostam, Jafar Univ Tabriz Dept Elect & Comp Engn Tabriz Iran

Reducing the computational complexity of the modern wireless communication systems such as massive MIMO configurations is of utmost interest. In this paper, we propose algorithms which can be used to accelerate matrix inversion and reduce the complexity of common spatial multiplexing schemes in massive MIMO systems. Here, we specifically investigate the performance of the proposed methods in systems that utilize STBC (Space-Time Block Code) in the uplink of dynamic massive MIMO systems for different scenarios. A multi-user system in which the base station is equipped with a large number of antennas and each user has two antennas is considered. In addition, users can enter or exit the system dynamically. For a given space-time block coding/decoding scheme, the computational complexity of the receiver will be significantly reduced by employing the proposed methods. The first approach is utilizing Neumann series to approximate the inverse matrix for linear decoders. The second tactic is reducing the computational complexity of the STBC decoders when a user is added to system or removed from it. In the proposed schemes, the matrix inversion for ZF and MMSE decoding is derived from inversing a partitioned matrix and Woodbury matrix identity. Furthermore, the suggested techniques can be utilized when the number of users is fixed but the CSI changes for a particular user. The mathematical equations for both approaches are derived and the complexity of the suggested methods is compared to the direct computation of the inverse matrix. Moreover, the performance of the proposed algorithms is evaluated in terms of the system BER (bit error rate). Evaluations confirm the effectiveness of the proposed approaches.

关键词： Fast update linear decoder Massive MIMO Neumann series STBC Woodbury formula

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On the Design of Symmetric Entropy-Constrained Multiple Description Scalar Quantizer With linear Joint decoders

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IEEE TRANSACTIONS ON COMMUNICATIONS 2017年第8期65卷 3453-3466页

作者： Wu, Huihui Zheng, Ting Dumitrescu, Sorina McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada IBM China Beijing 100101 Peoples R China

This paper addresses the design of symmetric entropy-constrained multiple description scalar quantizers (EC-MDSQ) with linear joint decoders, i.e., where some of the decoders compute the reconstruction by averaging the reconstructions of individual descriptions. Thus, the use of linear decoders reduces the space complexity at the decoder since only a subset of the codebooks needs to be stored. The proposed design algorithm locally minimizes the Lagrangian, which is a weighted sum of the expected distortion and of the side quantizers' rates. The algorithm is inspired by the EC-MDSQ design algorithm proposed by Vaishampayan and Domaszewicz, and it is adapted from two to K descriptions. Differently from the aforementioned work, the optimization of the reconstruction values can no longer be performed separately at the decoder optimization step. Interestingly, we show that the problem is a convex quadratic optimization problem, which can be efficiently solved. Moreover, the generalization of the encoder optimization step from two to K descriptions increases drastically the amount of computations. We show how to exploit the special form of the cost function conferred by the linear joint decoders to significantly reduce the time complexity at this step. We compare the performance of the proposed design with multiple description lattice vector quantizers (MDLVQ) and with the multiple description scheme based on successive refinement and unequal erasure protection (UEP). Our experiments show that the proposed approach outperforms MDLVQ with dimension 1 quantization, as expected. Additionally, when more codebooks are added our scheme even beats MDLVQ with quantization dimension approaching 8, for rates sufficiently high. Furthermore, the proposed approach is also superior to UEP with dimension 1 quantization when the rates are low.

关键词： Multiple descriptions scalar quantization entropy-constrained linear decoder convex quadratic problem

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UNIQUE: Unsupervised Image Quality Estimation

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IEEE SIGNAL PROCESSING LETTERS 2016年第10期23卷 1414-1418页

作者： Temel, Dogancan Prabhushankar, Mohit AlRegib, Ghassan Georgia Inst Technol Sch Elect & Comp Engn Ctr Signal & Informat Proc Atlanta GA 30332 USA

In this letter, we estimate perceived image quality using sparse representations obtained from generic image databases through an unsupervised learning approach. A color space transformation, amean subtraction, and a whitening operation are used to enhance descriptiveness of images by reducing spatial redundancy;a linear decoder is used to obtain sparse representations;and a thresholding stage is used to formulate suppression mechanisms in a visual system. A linear decoder is trained with 7 GB worth of data, which corresponds to 100 000 8 x 8 image patches randomly obtained from nearly 1000 images in the ImageNet 2013 database. A patch-wise training approach is preferred to maintain local information. The proposed quality estimator UNIQUE is tested on the LIVE, the Multiply Distorted LIVE, and the TID 2013 databases and compared with 13 quality estimators. Experimental results show that UNIQUE is generally a top performing quality estimator in terms of accuracy, consistency, linearity, and monotonic behavior.

关键词： Color spaces linear decoder sparse representations suppression mechanisms unsupervised learning

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Activity Correlations between Direction-Selective Retinal Ganglion Cells Synergistically Enhance Motion Decoding from Complex Visual Scenes

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NEURON 2019年第5期101卷 963-+页

作者： Kuehn, Norma Krystyna Gollisch, Tim Univ Med Ctr Gottingen Dept Ophthalmol D-37073 Gottingen Germany Bernstein Ctr Computat Neurosci Gottingen D-37077 Gottingen Germany Neuroelect Res Flanders B-3001 Leuven Belgium

Neurons in sensory systems are often tuned to particular stimulus features. During complex naturalistic stimulation, however, multiple features may simultaneously affect neuronal responses, which complicates the readout of individual features. To investigate feature representation under complex stimulation, we studied how direction-selective ganglion cells in salamander retina respond to texture motion where direction, velocity, and spatial pattern inside the receptive field continuously change. We found that the cells preserve their direction preference under this stimulation, yet their direction encoding becomes ambiguous due to simultaneous activation by luminance changes. The ambiguities can be resolved by considering populations of direction-selective cells with different preferred directions. This gives rise to synergistic motion decoding, yielding more information from the population than the summed information from single-cell responses. Strong positive response correlations between cells with different preferred directions amplify this synergy. Our results show how correlated population activity can enhance feature extraction in complex visual scenes.

关键词： vision retinal ganglion cells direction selectivity texture motion feature extraction population code synergy model linear decoder correlations

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Population codes in the visual cortex

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NEUROSCIENCE RESEARCH 2013年第3期76卷 101-105页

作者： Tanabe, Seiji Yeshiva Univ Albert Einstein Coll Med Dept Neurosci Bronx NY 10461 USA

Every sensory event elicits activity in a broad population of cells that is distributed within and across cortical areas. How these neurons function together to represent the sensory environment is a major question in systems neuroscience. A number of proposals have been made, and recent advances in multi-neuronal recording have begun to allow researchers to test the predictions of these population-coding theories. In this review, I provide an introduction to some of the key concepts in population coding and describe several studies in the recent literature. The focus of this review is on sensory representation in the visual cortex and related perceptual decisions. The frameworks used to study population coding include population vectors, linear decoders, and Bayesian inference. Simple examples are provided to illustrate these concepts. Testing theories of population coding is an emerging subject in systems neuroscience, but advances in multi-neuronal recording and analysis suggest that an understanding is within reach. (c) 2013 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

关键词： Population code Visual cortex Perceptual decision linear decoder Bayesian inference

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Universal A Posteriori Metrics Game

Universal A Posteriori Metrics Game

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IEEE Information Theory Workshop (ITW)

作者： Abbe, Emmanuel Pulikkoonattu, Rethnakaran Ecole Polytech Fed Lausanne LCM CH-1015 Lausanne Switzerland Broadcom Inc Irvine CA 92604 USA

ISBN: (纸本)9781424482641

Over binary input channels, the uniform distribution is a universal prior, in the sense that it maximizes the worst case mutual information of all binary input channels and achieves at least 94.2% of the capacity. In this paper, we address a similar question. We look for the best collection of finitely many a posteriori metrics, to maximize the worst case mismatched mutual information achieved by decoding with these metrics (instead of an optimal decoder such as the Maximum Likelihood (ML) tuned to the true channel). It is shown that for binary input and output channels, two metrics suffice to actually achieve the same performance as an optimal decoder. In particular, this implies that there exist a decoder which is generalized linear and achieves at least 94.2% of the compound capacity on any compound set, without knowledge of the underlying set.

关键词： binary codes binary input channel channel capacity channels capacity decoding game theory linear codes linear decoder maximum likelihood decoding maximum likelihood estimation optimal decoder universal a posteriori metrics game

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Loop Closure Detection for Visual SLAM Based on Deep Learning 7

Loop Closure Detection for Visual SLAM Based on Deep Learnin...

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7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)

作者： Hu, Hang Zhang, Yunzhou Duan, Qiang Hu, Meiyu Pang, Linzhuo Northeastern Univ Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9781538604908

Simultaneous Localization and Mapping(SLAM) is used to solve the problem of mobile robot navigation and map building in an unknown environment. Loop closure detection is the key part of SLAM, which determines the precision and stability of SLAM to a great extent. Closed loop detection is affected by changes in light and dynamic environment. In order to improve the accuracy of the loop closure detection, this paper introduces the model of deep learning to transform the visual SLAM process. On the basis of Sparse Autoencoder(SAE), the activation function of the output layer is changed to the identity function to solve the problem that the input sample needs to be scaled and does not apply to the color image. Then, the input image and the trained network do convolution and pooling operation. Not only can significantly reduce the feature dimension, but also can improve the effect of feature description. The test conducted by using the open data set show s that the proposed method can effectively solve the problem of visual SLAM loop closure detection.

关键词： robot SLAM loop closure detection deep learning linear decoder

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Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface

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NEURON 2021年第19期109卷 3164-+页

作者： Nason, R. Samuel Mender, J. Matthew Vaskov, K. Alex Willsey, S. Matthew Kumar, Nishant Ganesh Kung, A. Theodore Patil, G. Parag Chestek, A. Cynthia Univ Michigan Dept Biomed Engn Ann Arbor MI 48109 USA Univ Michigan Robot Grad Program Ann Arbor MI 48109 USA Univ Michigan Sch Med Dept Neurosurg Ann Arbor MI 48109 USA Univ Michigan Dept Surg Sect Plast Surg Ann Arbor MI 48109 USA Univ Michigan Sch Med Dept Neurol Ann Arbor MI 48109 USA Univ Michigan Grad Program Neurosci Ann Arbor MI 48109 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

Modern brain-machine interfaces can return function to people with paralysis, but current upper extremity brain-machine interfaces are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a non-prehensile task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined. During online brain control, the ReFIT Kalman filter could predict individuated finger group movements with high performance. Next, training ridge regression decoders with individual movements was sufficient to predict untrained combined movements and vice versa. Finally, we compared the postural and movement tuning of finger-related cortical activity to find that individual cortical units simultaneously encode multiple behavioral dimensions. Our results suggest that linear decoders may be sufficient for brain-machine interfaces to execute highdimensional tasks with the performance levels required for naturalistic neural prostheses.

关键词： brain-machine interface intracortical primary motor cortex hand prosthesis linear decoder multiple simultaneous targets

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