High Noise Tolerance Research Articles

Predicting MoRFs in protein sequences using HMM profiles.

BackgroundIntrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task.MethodsIn this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others).ResultsTo evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors.ConclusionsUsing HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.

A Novel Classification Technique of Arteriovenous Fistula Stenosis Evaluation Using Bilateral PPG Analysis.

The most common treatment for end-stage renal disease (ESRD) patients is the hemodialysis (HD). For this kind of treatment, the functional vascular access that called arteriovenous fistula (AVF) is done by surgery to connect the vein and artery. Stenosis is considered the major cause of dysfunction of AVF. In this study, a noninvasive approach based on asynchronous analysis of bilateral photoplethysmography (PPG) with error correcting output coding support vector machine one versus rest (ESVM-OVR) for the degree of stenosis (DOS) evaluation is proposed. An artificial neural network (ANN) classifier is also applied to compare the performance with the proposed system. The testing data has been collected from 22 patients at the right and left thumb of the hand. The experimental results indicated that the proposed system could provide positive predictive value (PPV) reaching 91.67% and had higher noise tolerance. The system has the potential for providing diagnostic assistance in a wearable device for evaluation of AVF stenosis.

An effective carrier phase estimation scheme in faster than Nyquist WDM transmission system

We propose a carrier phase estimation method using unequaled digital pilot tone in a faster than Nyquist WDM transmission system, in which the digital pilot tone is employed to estimate laser phase noise induced by the lasers located at the transmitter and the receiver ends. The method can effectively overcome the scale of the inter-symbol interference introduced by FTN signaling, with the good features of high phase noise tolerance and simple hardware complexity.

OPTICAL CHARACTER RECOGNITION USING ARTIFICIAL NEURAL NETWORK

Optical character recognition refers to the process of translat ing images of hand-written, typewritten, or printed text into a format understood by machines for the purpose of editing, indexing/searching, and a reduction in storage size. Optical character recognition is the mechanical or electronic translation of images of handwritten, typewritten or printed text into machine-editable text. Artificial neural networks are commonly used to perform character recognition due to their high noise tolerance. In this paper, an Optical character recognition based on Artificial Neural Networks (ANNs). The ANN is trained using the Back Propagation algorithm.

An effective scheme of optical pilot aided carrier phase estimation for a time packing Nyquist optical communication system

An optical domain pilot aided carrier phase estimation (PA-CPE) scheme in a double-carrier time packing Nyquist system is proposed, in which the middle pilot can be used for carrier phase estimation in both side time packing signal channels. The proposed PA-CPE method can effectively overcome the scale of the inter-symbol interference (ISI) introduced by time packing, with the good characteristics of high phase noise tolerance and simple hardware complexity. The comprehensive mathematical descriptions of laser phase noise model and PA-CPE scheme are provided. The analyses show that the proposed PA-CPE can estimate the phase noise successfully despite ISI caused by time packing. In the double-carrier time packing Nyquist system mentioned above, using the proposed PA-CPE algorithm, the linewidth tolerance value of 6×10−5 can be reached with 1dB OSNR penalty at the bit error rate (BER) of 1×10−3 even though the compression factor α is as small as 0.625.

A 600 V High-Voltage IC Technique With a New Self-Shielding Structure for High Noise Tolerance and Die Shrink

A novel 600 V high-voltage IC (HVIC) featuring a high noise tolerance is proposed. The purpose of the proposed HVIC is to achieve the high noise tolerance without an increase of the fabrication cost. The basic device concept is to arrange a P− separation layer around the high-side control part, which is called a new self-shielding structure, to reduce a hole current injection under the condition of negative transient voltage noise. By applying the new self-shielding structure in the HVIC, more than $3\times $ higher noise tolerance (−95 V/ $1~\mu \text{s}$ ) and 20% die shrink can be obtained compared with a conventional HVIC, without additional fabrication process. This means the noise tolerance of the fabricated HVIC with proposed structure is high enough to be applied to over 600 V/50-A class power conversion applications. In this paper, the new self-shielding concept of the proposed 600 V-class HVIC is presented with the simulation and experimental results.

Computational identification of MoRFs in protein sequences.

Intrinsically disordered regions of proteins play an essential role in the regulation of various biological processes. Key to their regulatory function is the binding of molecular recognition features (MoRFs) to globular protein domains in a process known as a disorder-to-order transition. Predicting the location of MoRFs in protein sequences with high accuracy remains an important computational challenge. In this study, we introduce MoRFCHiBi, a new computational approach for fast and accurate prediction of MoRFs in protein sequences. MoRFCHiBi combines the outcomes of two support vector machine (SVM) models that take advantage of two different kernels with high noise tolerance. The first, SVMS, is designed to extract maximal information from the general contrast in amino acid compositions between MoRFs, their surrounding regions (Flanks), and the remainders of the sequences. The second, SVMT, is used to identify similarities between regions in a query sequence and MoRFs of the training set. We evaluated the performance of our predictor by comparing its results with those of two currently available MoRF predictors, MoRFpred and ANCHOR. Using three test sets that have previously been collected and used to evaluate MoRFpred and ANCHOR, we demonstrate that MoRFCHiBi outperforms the other predictors with respect to different evaluation metrics. In addition, MoRFCHiBi is downloadable and fast, which makes it useful as a component in other computational prediction tools. http://www.chibi.ubc.ca/morf/.

An adaptive hybrid pattern for noise-robust texture analysis

Local binary patterns (LBP) achieve great success in texture analysis, however they are not robust to noise. The two reasons for such disadvantage of LBP schemes are (1) they encode the texture spatial structure based only on local information which is sensitive to noise and (2) they use exact values as the quantization thresholds, which make the extracted features sensitive to small changes in the input image. In this paper, we propose a noise-robust adaptive hybrid pattern (AHP) for noised texture analysis. In our scheme, two solutions from the perspective of texture description model and quantization algorithm have been developed to reduce the feature׳s noise sensitiveness. First, a hybrid texture description model is proposed. In this model, the global texture spatial structure which is depicted by a global description model is encoded with the primitive microfeature for texture description. Second, we develop an adaptive quantization algorithm in which equal probability quantization is utilized to achieve the maximum partition entropy. Higher noise-tolerance can be obtained with the minimum lost information in the quantization process. The experimental results of texture classification on two texture databases with three different types of noise show that our approach leads significant improvement in noised texture analysis. Furthermore, our scheme achieves state-of-the-art performance in noisy face recognition.

Transient Wave-blockage Interaction in Pressurized Water Pipelines

Transient flows are commonly encountered in pressurized water pipelines and have been studied for the purpose of systems design as well as defects detection and management. The representation and analysis of the transient response in the frequency domain is an attractive approach for fault detection due to its high noise tolerance and the linearized equations allows for the clear analytical characterization of system behaviour. Recent studies have demonstrated that an extended partial constriction of the pipe flow area causes changes in the system resonant frequencies and these changes can be used for locating and sizing partial blockages in pipes. Despite the successful application of this technique under field conditions, so far there is little work on the link between the changes in the system resonant conditions and the wave-blockage interaction. This paper provides a fundamental basis for the observation that unlike localised, discrete blockages, an extended blockage creates changes in the system resonant frequencies. Analytical methods using wave perturbation analysis and transfer matrix are shown in this study, and the obtained results are compared with numerical simulations and experimental data. The analytical analyses are used to explain the blockage-induced frequency shifts and to provide insights for its practical applications in pressurized water pipelines.

Multiplier-Free Phase Recovery With Polar-domain Multisymbol-Delay-Detector

We present a highly efficient carrier phase recovery module for coherent optical detection of single-carrier advanced modulation formats with differential precoding. Remarkably, we eliminate all multipliers from the multisymbol-delay-detector (MSDD) carrier recovery system, without incurring performance penalty relative to the previously shown complex-domain MSDD. We also introduce a novel polyblock hardware parallelization method for the MSDD, eliminating the “distant feedback” parallelization penalty of decision-feedback-based carrier recovery methods, such as MSDD. Simulations indicate extremely high nonlinear phase noise tolerance for the new polar MSDD carrier recovery system for 16-QAM 2000 km transmission. The ultralow-complexity CR is simulated and electrically demonstrated in real-time at 25 Gbaud symbol rate over FPGA HW for single-carrier QPSK and 16-QAM constellations, respectively.

A correlation analysis-based detection and delineation of ECG characteristic events using template waveforms extracted by ensemble averaging of clustered heart cycles

The goal of this study is to introduce a simple, standard and safe procedure to detect and to delineate P and T waves of the electrocardiogram (ECG) signal in real conditions. The proposed method consists of four major steps: (1) a secure QRS detection and delineation algorithm, (2) a pattern recognition algorithm designed for distinguishing various ECG clusters which take place between consecutive R-waves, (3) extracting template of the dominant events of each cluster waveform and (4) application of the correlation analysis in order to delineate automatically the P- and T-waves in noisy conditions. The performance characteristics of the proposed P and T detection–delineation algorithm are evaluated versus various ECG signals whose qualities are altered from the best to the worst cases based on the random-walk noise theory. Also, the method is applied to the MIT-BIH Arrhythmia and the QT databases for comparing some parts of its performance characteristics with a number of P and T detection–delineation algorithms.The conducted evaluations indicate that in a signal with low quality value of about 0.6, the proposed method detects the P and T events with sensitivity Se=85% and positive predictive value of P+=89%, respectively. In addition, at the same quality, the average delineation errors associated with those ECG events are 45 and 63ms, respectively. Stable delineation error, high detection accuracy and high noise tolerance were the most important aspects considered during development of the proposed method.

Prediction of silver nanoparticles’ diameter in montmorillonite/chitosan bionanocomposites by using artificial neural networks

Artificial neural networks (ANNs) are computational tools that have found comprehensive utilization in solving many complex real world problems. Major benefits in using ANNs are their remarkable information-processing characteristics pertinent mainly to high parallelism, nonlinearity, fault and noise tolerance, and learning and generalization capabilities. An ANN approach is used to model the size of silver nanoparticles (Ag-NPs) in montmorillonite/chitosan bionanocomposites layers as a function of the silver nitrate concentration, reaction of temperature, chitosan percentage, and d-spacing of clay layers. The best ANN model is found and this final model is capable of predicting the size of nanosilver for a wide range of conditions with a mean absolute error of less than 0.004 and a regression error of about 1. Results obtained showed good ability predictive of neural network model for the prediction of the size of Ag-NPs in chemical reduction methods.

Pilot-Symbol Assisted and Code-Aided Phase Error Estimation for High-Order QAM Transmission

In this paper, we present a novel phase error estimation scheme for low-density parity-check (LDPC) coded high-order quadrature amplitude modulation (QAM) transmission systems, and we provide a theoretical analysis of its performance. The scheme uses known pilot symbols which are assumed to be placed at equally spaced positions, and phase errors occurred at these pilot symbol positions are first estimated. They are then interpolated over the other positions by using a Wiener filter (WF), and estimation accuracy is next refined by using an LDPC code-aided phase-locked loop (PLL). We present an efficient implementation of this two-stage, Pilot-Symbol-Assisted and Code-Aided (PSA/CA) scheme and evaluate its performance by means of power spectrum analysis. We determine the mean square error at each step of the PSA/CA scheme, and we find an optimal value of the noise equivalent loop bandwidth of the PLL under the assumption that local oscillator phase noise can be modeled as a single-pole single-zero model. We show through simulations that the PSA/CA scheme has a very high phase noise tolerance comparable to that of state-of-the-art phase estimation schemes and provides a computationally efficient alternative.

Quick noise-tolerant learning in a multi-layer memristive neural network

In this letter, a multi-layer memristive neural network in which memristors serve as memory factor and impact factor is built for the binary images learning. Unlike the traditional artificial neural networks, the memristive neural network is trained with noise samples for the introduction of image overlay. A similarity recognition based on the impact factor for binary images is employed to exhibit the function of the memristive neural network. The Simulinks and the experiments shows that the proposed memristive neural network has quick learning speed and high noise tolerance.

Moving Object Localization Using Sound-Based Positioning System with Doppler Shift Compensation

Sound-based positioning systems are a potential alternative low-cost navigation system. Recently, we developed such an audible sound-based positioning system, based on a spread spectrum approach. It was shown to accurately localize a stationary object. Here, we extend this localization to a moving object by compensating for the Doppler shift associated with the object movement. Numerical simulations and experiments indicate that by compensating for the Doppler shift, the system can accurately determine the position of an object moving along a non-linear path. When the object moved in a circular path with an angular velocity of 0 to 1.3 rad/s, it could be localized to within 25 mm of the actual position. Experiments also showed the proposed system has a high noise tolerance of up to −25 dB signal-to-noise ratio (SNR) without compromising accuracy.

Quantitative comparison of 3D and 2.5D gamma analysis: introducing gamma angle histograms

Comparison of dose distributions using the 3D gamma method is anticipated to provide better indicators for the quality assurance process than the 2.5D (stacked 2D slice-by-slice) gamma calculation, especially for advanced radiotherapy technologies. This study compares the accuracy of the 3D and 2.5D gamma calculation methods. 3D and 2.5D gamma calculations were carried out on four reference/evaluation 3D dose sample pairs. A number of analysis methods were used, including average gamma and gamma volume histograms. We introduce the concept of gamma-angle histograms. Noise sensitivity tests were also performed using two different noise models. The advantage of the 3D gamma method showed up as a higher proportion of points passing the tolerance criteria of 3% dose difference and 3 mm distance-to-agreement (DTA), with considerably lower average gamma values, a lower influence of the DTA criterion, and a higher noise tolerance. The 3D gamma approach is more reliable than the 2.5D approach in terms of providing comprehensive quantitative results, which are needed in quality assurance procedures for advanced radiotherapy methods.

Differential-read 8T SRAM cell with tunable access and pull-down transistors

A novel 8T SRAM cell with high stability and write speed is proposed. The tunable access transistors and pull-down transistors are used to favour the desired operations. During a write operation, the strength of access transistors is enhanced, resulting in improving noise-immunity and write speed. The strength of pull-down transistors is enhanced during the read operation, hence exhibiting a high noise tolerance. The experimental results show that the proposed 8T cell can provide approximately 1.28× improvement in read static noise margin (SNM) and 1.10× improvement in write SNM compared to the standard 6T cell at 0.9V in 65nm process. Moreover, it also achieves 1.52× faster write speed and higher process variation tolerance.

Microstructure segmentation using active contours—Possibilities and limitations

Image data often carries redundant or unnecessary information. Consider a hypothetical image containing a phase embedded into a matrix. If the shape of the phase is of interest then the matrix information is redundant. Identification and classification of the pixels belonging to the matrix and the phase is a process called image segmentation. Active contouring is an edge detection based segmentation method that produces results superior to that of traditional edge detectors. High noise tolerance and built-in functional flexibility offered by the active contouring models make it an attractive technique in various applications including data compression, tracking of growth of a phase, and boundary outlining for further three-dimensional volume reconstruction. In this paper, the factors impacting the final contours will be discussed and shown in practice. It will also be shown that the technique is especially attractive when employed to process complex real microstructure datasets with limited user interaction.

An Affinity Based Complex Artificial Immune System

This paper proposes an affinity based complex artificial immune system considering the fact that the different eptitopes located on the surface of antigen can be recognized by a set of different paratopes expressed on the surface of immune cells. A neighborhood set consisting of immune cells with different affinities to a certain input antigen is built to simulate the nature immune behavior. Furthermore, the complex numbers are adopted as the data representation, besides the weight between different layers. In the simulations, the recognition on transformation patterns is performed to illustrate that the proposed system is capable of recognizing the transformation patterns and it has obviously higher noise tolerance ability than the previous system models.

Application of Active Contours on Metallurgical Microstructures

Employing computer algorithms for finding and outlining the boundaries between phases or grain boundaries (referred to as edge detection) is a widely used technique employed as an intermediate step in microstructure analysis. Having an outlined region of interest enables the user to extract data about the region or use it in the reconstruction of three-dimensional models. Because traditional edge detection relies on a user-selected threshold value, the results often are subjective. Furthermore, traditional edge detection frequently results in outlines that are incomplete, requiring additional processing steps, such as edge linking and spur removal. Active contouring is an edge-detection-based technique that typically yields results superior to that of a traditional edge detector. High noise tolerance and built-in flexibilities of active contours make the technique desirable to use across a broad range of applications. Although initially used for meteorological applications, several uses of active contours are presented for metallurgical microstructural data obtained using optical and electron microscopies to demonstrate robustness and the range of applications that can employ active contours.