2018 IEEE Statistical Signal Processing Workshop (SSP)最新文献_第4页

Tempered Fractional Brownian Motion: Wavelet Estimation and Modeling of Turbulence in Geophysical Flows 调质分数布朗运动:地球物理流湍流的小波估计与模拟

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450714

B. C. Boniece, Farzad Sabzikar, G. Didier

{"title":"Tempered Fractional Brownian Motion: Wavelet Estimation and Modeling of Turbulence in Geophysical Flows","authors":"B. C. Boniece, Farzad Sabzikar, G. Didier","doi":"10.1109/SSP.2018.8450714","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450714","url":null,"abstract":"Fractional Brownian motion (fBm) is a Gaussian, stationary-increment process whose self-similarity property is governed by the so-named Hurst parameter H ∈ (0,1). FBm is one of the most widely used models of scale invariance, and its instance H = 1/3 corresponds to the classical Kolmogorov spectrum for the inertial range of turbulence. Tempered fractional Brownian motion (tfBm) was recently introduced as a new canonical model that displays the so-named Davenport spectrum, a model that also accounts for the low frequency behavior of turbulence. The autocorrelation of its increments displays semi-long range dependence, i.e., hyperbolic decay over moderate scales and quasi-exponential decay over large scales. The latter property has now been observed in many phenomena, from wind speed to geophysics to finance. This paper introduces a wavelet framework to construct the first estimation method for tfBm. The properties of the wavelet coefficients and spectrum of tfBm are studied, and the estimator’s performance is assessed by means of Monte Carlo experiments. We also use tfBm to model geophysical flow data in the wavelet domain and show that tfBm provides a closer fit than fBm.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122908801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

On the Timing Synchronization under 1-Bit Quantization and Oversampling 1位量化和过采样下的定时同步

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450761

Martin Schlüter, Meik Dörpinghaus, G. Fettweis

{"title":"On the Timing Synchronization under 1-Bit Quantization and Oversampling","authors":"Martin Schlüter, Meik Dörpinghaus, G. Fettweis","doi":"10.1109/SSP.2018.8450761","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450761","url":null,"abstract":"As the demand for communication systems with high data rates is increasing, large bandwidths, and thus high sampling rates, are required. As a consequence, the energy consumption of conventional high resolution analog-to-digital converters increases drastically. On the contrary, high resolution in time domain is less difficult to achieve than high resolution in amplitude domain. This motivates the design of communication systems with 1-bit quantization and oversampling. It has been shown that utilizing run-length limited sequences and faster-than-Nyquist signaling is beneficial in terms of achievable rate. However, it is an open question how receiver synchronization can be performed in such systems. In this work we assume perfect frame, frequency and phase synchronization and investigate the effect of a fixed but unknown time shift. Due to 1-bit quantization, standard timing estimation and interpolation cannot be applied. We show that oversampling w.r.t. the signaling rate compensates for the error introduced by the time shift. If the oversampling factor is an integer value, estimating the time shift becomes obsolete if the oversampling rate is sufficiently high.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127772115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Adaptive Step Size Momentum Method For Deconvolution 反卷积的自适应步长动量法

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450762

Trung Vu, R. Raich

引用次数: 2

Least-Squares Signal Synthesis From Modified S-Transform 基于改进s变换的最小二乘信号合成

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450851

Yazan Abdoush, G. Pojani, G. Corazza

引用次数: 2

Sparse Bayesian Learning for Directions of Arrival on an FPGA 基于FPGA的稀疏贝叶斯到达方向学习

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450684

H. Groll, C. Mecklenbräuker, P. Gerstoft

引用次数: 3

Signal-to-Noise-Ratio Analysis of Compressive Data Acquisition 压缩数据采集的信噪比分析

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450796

R. Pribic, G. Leus, Christos Tzotzadinis

引用次数: 5

Multi-Branch Binary Modulation Sequences For Interferer Rejection 用于抗干扰的多支路二进制调制序列

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450861

Dian Mo, Marco F. Duarte

{"title":"Multi-Branch Binary Modulation Sequences For Interferer Rejection","authors":"Dian Mo, Marco F. Duarte","doi":"10.1109/SSP.2018.8450861","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450861","url":null,"abstract":"When the techniques of random modulation are used in wideband communication receivers, one can design spectrally shaped sequences that mitigate interferers while preserving messages to reduce distortion caused by amplifier nonlinearity and noise. For sampling rates that are too high for standard modulation, one can instead rely on multi-branch architectures involving multiple modulators working at reduced sampling rates. In this paper, we propose an algorithm to design a set of binary sequences to be used in multi-branch modulation to mitigate a strong interferer while allowing for stable message recovery. The implementation consists of a quadratic program that is relaxed into a semidefinite program combined with a randomized projection. While interferer signals are often modeled as a subspace under the discrete Fourier transform, spectrum leakage occurs when the signal contains so-called off-grid frequencies. The Slepian basis provides a much better-suited representation for such bandlimited signals that mitigates spectrum leakage. We use both representations during the evaluation of our design algorithm, where numerical simulations show the advantages of our sequence designs versus the state of the art.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129507291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Sparse Power Factorization With Refined Peakiness Conditions 精细峰值条件下的稀疏功率分解

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450850

Dominik Stöger, Jakob Geppert, F. Krahmer

引用次数: 2

Data Clustering Using Matrix Factorization Techniques for Wireless Propagation Map Reconstruction 基于矩阵分解技术的数据聚类无线传播图重构

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450795

Junting Chen, U. Mitra

引用次数: 2

Secrecy Capacity Analysis of Transmit-Receive Diversity Systems 收发分集系统的保密能力分析

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450857

K. Maichalernnukul

引用次数: 1