Channel optimization of predictive VQ for spectrum coding

Research on Anti-printing & Scanning Watermark Algorithm Based on Spread Spectrum Coding

2009 International Conference on Information Technology and Computer Science ◽

10.1109/itcs.2009.267 ◽

2009 ◽

Author(s):

Zhang Jiuhua ◽

He Guangpu

Keyword(s):

Spread Spectrum ◽

Spectrum Coding ◽

Watermark Algorithm

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Efficient Skin Spectrum Coding Method

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) ◽

10.1109/bhi.2019.8834679 ◽

2019 ◽

Author(s):

Soyoung Lee ◽

Karam Choi ◽

Sung Hyun Nam

Keyword(s):

Coding Method ◽

Spectrum Coding

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A New Multiple-Symbol Differential Detection Strategy for Error-Floor Elimination of IEEE 802.15.4 BPSK Receivers Impaired by Carrier Frequency Offset

Wireless Communications and Mobile Computing ◽

10.1155/2019/5409612 ◽

2019 ◽

Vol 2019 ◽

pp. 1-26 ◽

Cited By ~ 2

Author(s):

Gaoyuan Zhang ◽

Congzheng Han ◽

Baofeng Ji ◽

Congyu Shi ◽

Ping Xie ◽

...

Keyword(s):

Spread Spectrum ◽

Carrier Frequency ◽

Ieee 802.15.4 ◽

Carrier Frequency Offset ◽

Frequency Offset ◽

Coherent Detection ◽

Differential Detection ◽

Error Floor ◽

Detection Strategy ◽

Spectrum Coding

In this paper, we pay our attention towards the noncoherent demodulation aspect of binary phase shift keying (BPSK) receivers for IEEE 802.15.4 wireless sensor networks (WSNs), and a carrier frequency offset invariant as well as error-floor free multiple-symbol differential detection (MSDD) strategy is proposed over the flat fading channel. This detector is an alternative to the multiple-symbol detector that has been considered almost exclusively in the past. In this new configuration, the receivers do not perform chip-level precompensation as in conventional scheme but bit-level postcompensation. That is, the bit-level autocorrelation operation is first implemented with the “raw” chip sample, and then the carrier frequency offset effect (CFOE) embedded in the achieved statistic is compensated. Correspondingly, the cumulative error in the detection metric is decreased so much that the pervasive error floor for the conventional MSDD scheme is suppressed. Also, complexity efficient estimators for the MSDD scheme are reinvestigated, analyzed, and summarized. Simulation results demonstrate that this new detection strategy may achieve rather more encouraging gain from differential and spread spectrum coding than the conventional single differential coherent detection (SDCD) scheme. The pervasive error floor is also eliminated as compared with conventional MSDD scheme even if the most simple estimator is configured under large bit observation length. Then, much transmitting energy may be saved for each chip symbol, which is practically desired for transmit-only nodes in WSNs.

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Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network-Based Remaining Useful Life Estimation of Bearings

Shock and Vibration ◽

10.1155/2020/8240168 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Yu Pang ◽

Limin Jia ◽

Zhan Liu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

High Efficiency ◽

Remaining Useful Life ◽

Discrete Cosine Transformation ◽

Time Frequency ◽

Cosine Transformation ◽

Useful Life ◽

Spectrum Coding ◽

Low Dimensional

In recent years, several time-frequency representation (TFR) and convolutional neural network- (CNN-) based approaches have been proposed to provide reliable remaining useful life (RUL) estimation for bearings. However, existing methods cannot tackle the spatiotemporal continuity between adjacent TFRs since temporal proposals are considered individually and their temporal dependencies are neglected. In allusion to this problem, a novel prognostic approach based on discrete cosine transformation (DCT) and temporal adjacent convolutional neural network (TACNN) is proposed. Wavelet transform (WT) is applied to effectively map the raw signals to the time frequency domain. Considering the high load and complexity of model computation, bilinear interpolation and DCT algorithm are introduced to convert TFRs into low-dimensional DCT spectrum coding matrix with strong sparsity. Furthermore, the TACNN model is proposed which is capable of learning discriminative features for temporal adjacent DCT spectrum coding matrix. Effectiveness of the proposed method is verified on the PRONOSTIA dataset, and experiment results show that the proposed model is able to realize automatic high-precision estimation of bearings RUL with high efficiency.

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