Autocorrelation Sequence Research Articles

Sequence Features and Subset Selection Technique for the Prediction of Protein Trafficking Phenomenon in Eukaryotic Non Membrane Proteins

Protein trafficking or protein sorting is the mechanism by which a cell transports proteins to the appropriate position in the cell or outside of it. This targeting is based on the information contained in the protein. Many methods predict the subcellular location of proteins in eukaryotes from the sequence information. However, most of these methods use a flat structure to perform prediction. In this work, we introduce ensemble methods to predict locations in the eukaryotic protein-sorting non membrane pathway hierarchically. We used features that were extracted exclusively from full length protein sequences with feature subset selection for classification. Sequence driven features, sequence mapped features and sequence autocorrelation features were tested with ensemble learners and classifier performances were compared with and without feature subset selection technique. This study shows the new features extracted from full length eukaryotic protein sequences are effective at capturing biological features among compartments in eukaryotic non membrane pathways at two levels. Feature subset selection techniques helped to reduce the time taken for building the classification model.

Bagging with CTD – A Novel Signature for the Hierarchical Prediction of Secreted Protein Trafficking in Eukaryotes

Protein trafficking or protein sorting in eukaryotes is a complicated process and is carried out based on the information contained in the protein. Many methods reported prediction of the subcellular location of proteins from sequence information. However, most of these prediction methods use a flat structure or parallel architecture to perform prediction. In this work, we introduce ensemble classifiers with features that are extracted directly from full length protein sequences to predict locations in the protein-sorting pathway hierarchically. Sequence driven features, sequence mapped features and sequence autocorrelation features were tested with ensemble learners and their performances were compared. When evaluated by independent data testing, ensemble based-bagging algorithms with sequence feature composition, transition and distribution (CTD) successfully classified two datasets with accuracies greater than 90%. We compared our results with similar published methods, and our method equally performed with the others at two levels in the secreted pathway. This study shows that the feature CTD extracted from protein sequences is effective in capturing biological features among compartments in secreted pathways.

Equivalent Quasi-Convex Form of the Multicast Max–Min Beamforming Problem

Multicast downlink transmission in a multicell network with multiple users is investigated. Max–min beamforming (MB) enables a fair distribution of the signal-to-interference-plus-noise ratio (SINR) among all users in a network for given power constraints at the base stations (BSs) of the network. The multicast MB problem (MBP) is proven to be NP-hard and nonconvex in general. However, the MBP has an equivalent quasi-convex (QC) form and can be optimally solved with an efficient algorithm for special instances, depending on the structure of the available channel state information (CSI). This paper derives the equivalent QC form of the MBP for the practically relevant scenario of long-term CSI in the form of Hermitian positive semi-definite Toeplitz (HPST) matrices and per-antenna array power constraints. The optimization problem is then given by a convex feasibility check problem with finite autocorrelation sequences (FASs) as optimization variables. Using FASs, the MBP can be expressed as a QC fractional program (FP). Based on the theory of QC programming, this paper proposes a fast root-finding algorithm with superlinear convergence.

Autocorrelations of Binary Sequences and Run Structure

We analyze the connection between the autocorrelation of a binary sequence and its run structure given by the run length encoding. We show that both the periodic and the aperiodic autocorrelation of a binary sequence can be formulated in terms of the run structure. The run structure is given by the consecutive runs of the sequence. Let C = (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,..., C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) denote the autocorrelation vector of a binary sequence and Δ the difference operator. We prove that the th component of Δ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (C) can be directly calculated by using the consecutive runs of total length k. In particular, this shows that the th autocorrelation is already determined by all consecutive runs of total length l <; k.In the aperiodic case, we show how the run vector can be efficiently calculated and give a characterization of skew-symmetric sequences in terms of their run length encoding.

Analytical expressions for power spectral density issued from one-dimensional continuous piecewise linear maps with three slopes

During the two last decades, chaotic signals have been increasingly considered in telecommunications, signal processing or secure transmissions. Despite the importance of frequency in the telecommunications and transmission security there are few works that explore the spectrum of chaotic signals by considering the bandwidths. Previous works have concerned skew tent maps. In this paper, we derive analytical expressions for the autocorrelation sequence (ACS) and power spectral density (PSD) of chaotic signals generated by one-dimensional continuous piecewise linear maps with three slopes. We obtain similar results to those obtained using the skew-tent map. Our results permit to conjecture that the bandwidth of chaotic signals obtained from piecewise linear continuous map can have the same behaviour whatever be the number of slopes in the map.

Biometric key distribution solution with energy distribution information of physiological signals for body sensor network security

Recently, a kind of lightweight and resource-efficient biometrics-based security solutions were proposed for the emerging body sensor network (BSN). In such security solutions, physiological characteristics that can be captured by individual sensors of BSN were proposed to generate entity identifiers (EIs) for securing keying materials by a biometric approach. In this study, the authors focus on an improved key distribution solution with the energy distribution information of physiological signals (EDPSs) -based EIs. Firstly, different EDPS-based EI generation schemes are studied. Based on the existing multi-windows Fourier transform scheme, a modified one with single-window is proposed to improve the identification performance of the generated EIs. Then, a different method based on the discrete cosine transform of the autocorrelation sequence of physiological signals is proposed aiming for a significant increase in identification rates. The performances of time-varying randomness and identification rates are evaluated to examine EI's feasibility in securing the transmission of keying materials. Based on the characteristics of generated EIs, the corresponding key distribution solution, that is, user-dependent fuzzy vault, is proposed. A detailed system performance analysis in terms of half total error rate, anti-attack ability, as well as computational complexity, is conducted to demonstrate the effectiveness of the proposed solution.

Nonlinear Structure and Autocorrelation of Nonlinear Sequences

Nonlinear Structure and Autocorrelation of Nonlinear Sequences

Research on perfect binary correlation sequences based on higher-order cumulant

Current researches define the perfect signal by the inner product of sequence itself and its delay sequence. However, these traditional sequences based on second-order statistics cannot handle colored Gaussian measurement noise automatically. Because higher-order cumulant (HOC) is insensitive to the adding Gaussian noise and symmetrical non-Gaussian noise, a new kind of perfect binary signal with good periodic correlation function based on HOC is presented. This paper proposes a new concept of perfect binary-third-order cyclic autocorrelation sequences (PBTOCAS) and defines the quasi-perfect binary-third-order cyclic autocorrelation sequences (QPBTOCAS) and the almost perfect binary-third-order cyclic autocorrelation sequences (APBTOCAS). Then the properties of these binary sequences are studied, and we theoretically prove that binary-third-order cyclic autocorrelation sequences can effectively suppress colored Gaussian noise. Finally, some QPBTOCAS and APBTOCAS with short lengths by computer searching are listed. From the observation of the PBTOCAS, we can see that it can well suit engineering applications, remedying the defect of the conventional pseudo-noise code used in very low signal-noise-ratio environments.

D2D 통신 시스템을 위한 CAZAC 시퀀스 기반 링크 스케줄링 기법

대표적인 D2D (Device-to-Device) 통신 시스템 중 하나인 Qualcomm사의 FlashLinQ 시스템에서는 링크 스케줄링 과정을 낮은 복잡도로 실현할 수 있도록 하기 위해 단일-톤 (single-tone) 신호를 이용한 우선순위 및 SIR (Signal-to-Interference power Ratio) 기반의 링크 스케줄링 기법을 수행한다. 하지만 다중 경로 채널 환경에서는 주파수 선택적 페이딩의 영향으로 단일-톤 위치에서와 실제 데이터가 전송되는 전체 대역에서의 수신 전력 간 오차가 발생할 수 있으며, 이는 공평성 측면에서 문제가 될 뿐만 아니라 셀 전체 전송률 상의 손실을 일으킬 수 있다. 따라서, 본 논문에서는 이러한 문제를 해결하기 위해 CAZAC (Constant Amplitude Zero Auto-Correlation) 시퀀스의 상관 특성을 이용해 전체 대역에 대한 SIR 에 가까운 값을 획득할 수 있는 링크 스케줄링 기법을 제안한다. 제안 기법은 전체 대역을 다수의 sub-block 으로 구분하고 각 sub-block 마다 링크의 우선순위에 해당하는 순환 오프셋 (cyclic offset) 을 적용한 CAZAC 시퀀스를 전 대역에 걸쳐 전송하여, 수신 신호와 참조 신호간의 순환 상호 상관 연산 (cyclic cross-correlation)을 통해 전체 대역에 대한 SIR 에 근접한 값을 획득할 수 있다. FlashLinQ, one of the typical D2D communication systems developed by Qualcomm, considers a single-tone communication based distributed channel-aware link scheduling method to realize the link scheduling process with low control overheads. However, considering the frequency selective fading effect of practical multi-path channel, the single-tone based SIR estimation causes a critical scheduling error problem because the received single-tone signal has quite different channel gain at each sub-carrier location. In order to overcome this problem, we propose a novel link scheduling method based on CAZAC (Constant Amplitude Zero Auto-Correlation) sequence for D2D communication system. In the proposed method, each link has a unique offset value set for the generation of CAZAC sequences. CAZAC sequences with the cyclic offsets are transmitted using multiple sub-blocks in the entire bandwidth, and then each device can obtain nearly full-band SIR using a good cyclic cross-correlation property of CAZAC sequence.

Study on Construction of a Class Three-level of Autocorrelation Sequence Pairs

Study on Construction of a Class Three-level of Autocorrelation Sequence Pairs

New construction of perfect sequence set and low correlation zone sequence set

For a given binary ideal autocorrelation sequence, we construct a perfect sequence set by changing a few bits of the sequence. The set has a large size with respect to the period of its sequences. Based on the constructed perfect sequence set, a new class of low correlation zone sequence sets whose low correlation zone length can be chosen flexibly is obtained. Moreover, the new constructed low correlation zone sequence sets can attain Tang-Fan-Matsufuji’s bound with suitably chosen parameters.

New features using robust MVDR spectrum of filtered autocorrelation sequence for robust speech recognition.

This paper presents a novel noise-robust feature extraction method for speech recognition using the robust perceptual minimum variance distortionless response (MVDR) spectrum of temporally filtered autocorrelation sequence. The perceptual MVDR spectrum of the filtered short-time autocorrelation sequence can reduce the effects of residue of the nonstationary additive noise which remains after filtering the autocorrelation. To achieve a more robust front-end, we also modify the robust distortionless constraint of the MVDR spectral estimation method via revised weighting of the subband power spectrum values based on the sub-band signal to noise ratios (SNRs), which adjusts it to the new proposed approach. This new function allows the components of the input signal at the frequencies least affected by noise to pass with larger weights and attenuates more effectively the noisy and undesired components. This modification results in reduction of the noise residuals of the estimated spectrum from the filtered autocorrelation sequence, thereby leading to a more robust algorithm. Our proposed method, when evaluated on Aurora 2 task for recognition purposes, outperformed all Mel frequency cepstral coefficients (MFCC) as the baseline, relative autocorrelation sequence MFCC (RAS-MFCC), and the MVDR-based features in several different noisy conditions.

Robust Modeling of Low-Cost MEMS Sensor Errors in Mobile Devices Using Fast Orthogonal Search

Accessibility to inertial navigation systems (INS) has been severely limited by cost in the past. The introduction of low-cost microelectromechanical system-based INS to be integrated with GPS in order to provide a reliable positioning solution has provided more wide spread use in mobile devices. The random errors of the MEMS inertial sensors may deteriorate the overall system accuracy in mobile devices. These errors are modeled stochastically and are included in the error model of the estimated techniques used such as Kalman filter or Particle filter. First-order Gauss-Markov model is usually used to describe the stochastic nature of these errors. However, if the autocorrelation sequences of these random components are examined, it can be determined that first-order Gauss-Markov model is not adequate to describe such stochastic behavior. A robust modeling technique based on fast orthogonal search is introduced to remove MEMS-based inertial sensor errors inside mobile devices that are used for several location-based services. The proposed method is applied to MEMS-based gyroscopes and accelerometers. Results show that the proposed method models low-cost MEMS sensors errors with no need for denoising techniques and using smaller model order and less computation, outperforming traditional methods by two orders of magnitude.

The Study of Local Wave Noise Reduction Based on the Correlation Analysis

The signal can be divided into a number of intrinsic mode components (IMF) through local wave decomposition. The decomposition process is equivalent to the adaptive filter for signal. The frequencies of each IMF have reduced with the decomposition order. This paper established judgment criteria of signal, noise and pseudo component based on the local wave decomposition, used the characteristics of cross-correlation coefficient and autocorrelation sequence, combined with the power spectral density. The dominant mode function was extracted effectively and the final effects are used gear reducer in the JZQ250 of fault rolling bearing inner ring. It provides a new method for non-stationary signal adaptive noise reduction and fault diagnosis of rotating machinery.

Optimal design of perfect DFT sequences

In this paper, we propose a novel scheme to construct a large set with N(N+1) perfect sequences of length N, derived from an Inverse Discrete Fourier Transform (IDFT) of Chu and maximum-length sequences. This optimum set of perfect periodic autocorrelation sequences has a maximum absolute value of periodic cross-correlation strictly lower than N and close to the well known lower bound SQRT(N). Moreover, we present a method to transform these perfect sequences into orthogonal sequences. A similar method is also proposed to obtain optimum bipolar codes derived from an alternative set of N2 perfect sequences.In the design of perfect sequences, the difficulty is to achieve both low cross-correlation and low peak-to-average power ratio (PAPR). Many of the proposed perfect DFT sequences should have low PAPR and thus can be applied in an OFDM–CDMA (Orthogonal Frequency-Division Multiplexing–Code Division Multiple Access) communication system or in a simple CDMA communication system. Alternatively, the proposed perfect DFT sequences may be useful for pre-coded mapping in OFDM communication systems or for the design of radar waveform diversity sets.

Method of Generating Multiple Sets of Orthogonal Codes with Wide Choice of Spreading Factors

This paper presents a generic method to construct multiple sets of orthogonal codes for application in direct sequence spread spectrum systems. The plurality of orthogonal sets is generated using any single two-level autocorrelation sequence of period one less than the desired spreading factor of the orthogonal codes. The proposed method hence generates orthogonal codes of all spreading factors for which a corresponding two-level autocorrelation sequence exists. The resulting orthogonal sets with wide choice of spreading factors is desirable in synchronous direct sequence spread spectrum systems. The sets can be generated such that they do not contain the codeword with single polarity as in the case of Walsh-Hadamard codes and orthogonal sets generated from cyclic shifts of two-level autocorrelation sequences. This renders all codes in the generated sets with an effective spreading gain. The generation method also encompasses a procedure to generate N-1 sets with low inter-set correlation for application to oversaturated spread spectrum systems.

SURVEILLANCE OF DIABETES PREVALENCE RATE THROUGH THE DEVELOPMENT OF A MARKOV-BASED CONTROL CHART

In the field of statistical process control (SPC), usually two issues are addressed; the variables and the attribute quality characteristics control charting. Focusing on discrete data generated from a process to be monitored, attributes control charts would be useful. The discrete data could be classified into two categories; the independent and auto-correlated data. Regarding the independence in the sequence of discrete data, the typical Shewhart-based control charts, such as p-chart and np-chart would be effective enough to monitor the related process. But considering auto-correlation in the sequence of the data, such control charts would not workanymore. In this paper, considering the auto-correlated sequence of X1, X2,…, Xt,… as the sequence of zeros or ones, we have developed a control chart based on a two-state Markov model. This control chart is compared with the previously developed charts in terms of the average number of observations (ANOS) measure. In addition, a case study related to the diabetic people is investigated to demonstrate the applicability and high performance of the developed chart.

The Triple Autocorrelation of an ${m}$-Sequence is a Lempel Costas Array

The triple autocorrelation of a sequence is shown to be a Lempel Costas array iff the sequence is maximal, assuming correlation is appropriately defined and the alphabet of the sequence is of prime size. A method that allows the construction of thumbtack autocorrelation sequences of arbitrary alphabet size is subsequently proposed.

A Note on "A Novel ZCZ Code Based on m-Sequences and its Applications in CDMA Systems"

This short communication shows that zero correlation zone sequences could be generated by using any odd-length binary sequence with ideal two-level autocorrelation function in place of the m-sequence using the exact same procedure proposed in the original paper. This removes the restriction on the spreading factor of the m-ZCZ sequences which could assume only the form 2n for some integer n. The spreading factor can now be any N for which an ideal two-level autocorrelation sequence of period N - 1 exists.

A Three-Valued Walsh Transform From Decimations of Helleseth–Gong Sequences

The Walsh transform of two-level autocorrelation sequences has played an important role in the construction of the set of sequences in which any two sequences are orthogonal. For p -ary sequences, there are only two basic classes of two-level autocorrelation sequences with no subfield structures for an arbitrary odd prime p. One is the class of p-ary m-sequences and the other is the class of p -ary Helleseth-Gong sequences. In this paper, the Walsh transform of a subclass of p -ary Helleseth-Gong sequences and the Walsh transform of their particular decimations are completely determined and are shown to be three-valued.