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This paper proposes a new way to compensate comparator errors in successive approximation analog-to-digital convertor (SAR ADC). The method adds negatively biased capacitance to traditional binary-scaled compensation, increasing ADC accuracy by up to 20%. A novel digital-to-analog convertor (DAC) structure is introduced to further increase its efficiency, which reduces total capacitance by 80%. According...
Estimating the location of sensor nodes in wireless sensor networks is a fundamental problem, as sensor node locations play a critical role in a variety of applications. In many cases the area covered is very large making it impossible to localize all sensor nodes using single-hop localization techniques. A solution to this problem is to use a multi-hop localization technique to estimate sensor node...
Modulation classification is a signal processing technique which can estimate the modulation format of the received signals using multiple hypotheses test In this paper, we have presented an overview of modulation classification techniques based on Goodness-of-Fit tests. We have discussed the classification performance of modulation classification method based on Anderson Darling (AD) test, Cramer...
Epileptic detection techniques rely heavily on the Electroencephalography (EEG) as a representative signal carrying valuable information pertaining to the current brain state. In this work, we investigate the stability of time domain EEG features while varying the channel conditions. We identify the feature sets that would provide the most robust EEG classification accuracy. Moreover, an embedded...
We consider the problem of source number detection based on uniform linear arrays (ULAs) and the recently proposed nested arrays. A ULA with N sensors can detect at most N − 1 sources, whereas a nested array provides O(N2) degrees of freedom with O(N) sensors, enabling us to detect K sources with N<K sensors. In order to make full use of the available limited valuable data, we propose a novel strategy,...
Voice activity detection algorithms are widely used in the areas of voice compression, speech synthesis, speech recognition, speech enhancement, and etc. In this paper, an efficient voice activity detection algorithm with sub-band detection based on time-frequency characteristics of mandarin is proposed. The proposed sub-band detection consists of two parts: crosswise detection and lengthwise detection...
A huge amount of microarray datasets are produced with big number of genes and small samples. Feature selection methods have become a very sharp tool to select the gene signatures from the whole gene set. In recent years, researchers are concerned much about the datasets containing samples of cancer as well as corresponding control tissues. However, few feature selection methods consider the effect...
This paper presents a general framework for distributed estimation of a dynamical process in a Wireless Sensor Network (WSN) in which the Sensor Nodes (SNs) communicate their measurements to the Fusion Center (FC) via a single hop wireless channel. The SNs adapt their sensing/transmission strategy based on a minimal feedback message provided by the FC, which informs them on the estimation quality...
The linear minimum mean-square error (LMMSE) estimation has been shown to provide a good tradeoff between the computational requirement and estimation accuracy in nonlinear point estimation. However, the best estimator within the linear class may not be adequate to provide acceptable accuracy when dealing with a highly nonlinear problem. A generalized LMMSE (GLMMSE) estimation framework searches for...
In the communications field, many methods have been introduced in order to improve the quality of signal reception. This is because various parameters can limit the quality of signal reception such as multipath fading and interference that results the signal with low signal-to-noise ratio (SNR) or no signal reception. This paper presents a multi-receiver system to improve signal reception and classification...
This paper presents a fast symbolic method for computation of the signal-to-noise ratio (SNR) of switched-capacitor sigma-delta modulators. The key idea is to use a Taylor expansion polynomial to approximate the rational expression of a noise transfer function (NTF). This new method can be used in automatic optimization tools developed for switched-capacitor sigma-delta modulator design.
A parameter estimation algorithm of linear frequency modulation (LFM) signal with high precision is presented here. It is firstly based on FFT and CAPON method for estimating the frequency, and then the frequency rate is estimated by using Dechirp which only searches in the frequency rate plane by DFT at the frequency estimate value. The algorithm consists of a coarse search and a fine search to reduce...
Considering low estimation accuracy and small synchronization range of carrier synchronization algorithms in low signal-to-noise ratio (SNR) for short burst communication systems, a joint pilot and iterative decoding soft-output carrier synchronization algorithm is proposed, which utilizes the time-domain correlation algorithm for coarse estimation and makes hard decision directly for systematic soft-information...
To ensure a satisfactory QoE (Quality of Experience) and facilitate system design in speech recognition services, it is essential to establish a method that can be used to efficiently investigate recognition performance in different noise environments. Previously, we proposed a performance estimation method using the PESQ (Perceptual Evaluation of Speech Quality) as a spectral distortion measure....
Carrier-frequency offset (CFO) estimation for orthogonal frequency-division multiplexing access (OFDMA) systems operating in multiuser uplink transmission is very challenging due to the presence of a multiple-parameter estimation problem. In this paper, we propose a novel blind CFO estimation method for interleaved OFDMA uplink based on distributed Bayesian compressive sensing (DBCS) theory. Considering...
This paper presents simple and accurate approximation of the normalized incomplete upper and lower gamma functions that frequently appear in Nakagami-m fading channels. Based on the proposed approximation and in conjunction with the approximation of the Q-function proposed by Chiani, Dardari, and Simon [1], simple and accurate generalized closed-form expressions for the average Bit Error Rate (BER)...
In this paper, we propose distribution based binary discriminative features and a novel feature enhancement process for automatic modulation classification. The new features exploit the signal distribution mismatch between two modulations. Signal distributions on I-Q segments, amplitude and phase, are considered to produce a comprehensive feature set for improved robustness. Logistic regression is...
One of the difficult and key problems which must be solved for fault prediction is to measure the credibility of fault prediction results. This paper uses Monte Carlo method to build SNR and prediction time model for the credibility of fault prediction. First, using satellite attitude control system as an example, fault prediction is achieved successfully. Then the curve between SNR, prediction time...
Many naturally occurring signals exhibit near, rather than perfect, periodicity, as a result of variation in cycle length (CL). The accuracy of methods to detect near-periodic signals is typically not evaluated against known CL variations which may compromise their performance. The maximum likelihood method (ML) proposed by Noll to evaluate periodicity involves block averaging which, with smoothing...
In this paper, a classifier using Fisher's Linear Discriminant Analysis is used to investigate the performance of three different extraction methods for brain signal based electroencephalogram (EEG)-P300. EEG-P300 recordings provide an important means of brain-computer communication, but their classification accuracy and transfer rate are limited by unexpected signal variations due to artifacts and...
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