Signal Processing Concepts Research Articles

Prediction and Identification of Cancer and Normal Genes Through Wavelet Transform Technique

Currently, in the field of bioinformatics the most important advanced practice of genome signal processing is used to analyze the biological sequences without involving any biological testing or treatments. To analyse the genome data with the help of signal processing concepts the technique of genome signal processing is being adopted. According to the earlier medical research reports, it was understood that the deadly disease of cancer is origin due to genetic abnormality. Some of the signal processing concepts such as Fourier transform, short term Fourier transform, wavelet transform, etc are used for analyzing the biological sequences. Wavelet transform is an important tool for analyzing the cancer genome. The main objective of this research article is to develop the necessary algorithm and to test for several databases available at National Center for Biotechnology Information. In this research the analysis was carried out by using MatlabR2015b for simulations which supports Bioinformatics toolbox. From the results it was found that, the coefficient of variation for cancer genes are always much higher than the normal genes.

Evaluation of a ZnS:6LiF based scintillation neutron detector at high counting rates

In this work we revisit the problem of the restricted count rate capability of thermal neutron detectors utilizing the ZnS scintillator . The effect of the long afterglow of the scintillator on the detector performance at high counting rates is studied. A count rate capability substantially above values currently regarded as feasible is demonstrated. • Afterglow of ZnS: 6 LiF so far regarded as limiting the count rate capability. • Concept of individual sensitive elements and signal processing introduced. • This concept overcomes the assumed limitation on count rate capability. • Count rates up to 80 kHz per readout channel reached at event losses below 10% • Count rate capability depends not only on dead time but also on trigger efficiency.

Recurrent neural network model with Bayesian training and mutual information for response prediction of large buildings

An accurate response prediction model is of great importance in various applications such as damage detection, structural health monitoring, and vibration control. Development of such a methodology for large civil structures is challenging because of their size and complicated behavior and noise-contaminated, nonlinear, and nonstationary nature of the signals. In addition, the prediction model must have a low computational burden for real-time applications. In this article, a new methodology and a nonlinear autoregressive exogenous model (NARX)-based recurrent neural network (NN) model is presented for accurate response prediction of large structures. The methodology is based on adroit integration of three concepts: a recent signal processing concept, empirical mode decomposition (EMD), mutual information (MI) index from the information theory, and a probabilistic Bayesian-based training algorithm. The EMD method is used to remove the noise in the measured signals. An MI index is proposed to determine the optimum number of neurons in the hidden layer of the NN model with the goal of reducing the computational requirements without affecting its performance. Finally, Bayesian regularization (BR) is proposed to train the optimized NN model. The effectiveness of the proposed methodology is assessed by predicting the structural response of a 1:20-scaled 38-story highrise building structure subjected to seismic excitations and ambient vibrations, and a five-story steel frame subjected to different levels of the Kobe earthquake.

Advances in Acoustic Signal Processing Techniques for Enhanced Bowel Sound Analysis.

With the invention of the electronic stethoscope and other similar recording and data logging devices, acoustic signal processing concepts and methods can now be applied to bowel sounds. In this paper, the literature pertaining to acoustic signal processing for bowel sound analysis is reviewed and discussed. The paper outlines some of the fundamental approaches and machine learning principles that may be used in bowel sound analysis. The advances in signal processing techniques that have allowed useful information to be obtained from bowel sounds from a historical perspective are provided. The document specifically address the progress in bowel sound analysis, such as improved noise reduction, segmentation, signal enhancement, feature extraction, localization of sounds, and machine learning techniques. We have found that advanced acoustic signal processing incorporating novel machine learning methods and artificial intelligence can lead to better interpretation of acoustic information emanating from the bowel.

Shared-photodetector readout to improve the sensitivity of positron emission tomography

Sensitivity is an important performance characteristic of positron emission tomography (PET) systems. Improved sensitivity can be used to reduce injected dose, reduce scan time, or improve the signal-to-noise ratio and temporal resolution for dynamic studies. One way to improve the sensitivity of PET scanners is to reduce the gaps between detector modules. In this paper, a new signal processing method, named the shared-photodetector readout method, is proposed and evaluated. In this method, the signals generated in nearest neighbor photodetectors adjacent to the detector module of interest, were used to help identify the interaction location in the detector module of interest. Using this method, scintillator array-based detector modules with almost 100% packing fraction can be built, and the edge crystals can be clearly resolved, even when the crystals are small compared to the photodetector size. To evaluate this signal processing concept in one dimension, a detector block with four dual-ended readout detector modules, was designed. The detector block consisted of eight 4 × 4 arrays of SensL MicroFJ-30035 SiPMs coupled to both ends of a 14 × 56 array of 0.9 × 0.9 × 20 mm3 LYSO elements with a pitch size of 0.96 mm and a length of 20 mm. Performance in terms of energy resolution, flood histogram, timing resolution and depth-of-interaction resolution obtained using the shared-photodetector readout method were compared to those obtained using a conventional readout method. The results show that better over-all performance was achieved using the shared-photodetector readout method, especially at the edges and corners of the array.

On signals processing on graphs, patterns, consensus and manifolds

The aim of this communication is to make several considerations regarding the way the notions of graph and Laplacian are involved in the concepts of signal processing on graphs, patterns, consensus and manifolds.

Application of dispersion entropy to status characterization of rotary machines

Nonlinear techniques have been widely and successfully used to analyze the vibration signals in rotary machines due to the nonlinear nature of such times series. We have recently introduced dispersion entropy (DisEn) as a powerful and fast nonlinear method to quantify the uncertainty of signals. This study investigates the usefulness of DisEn for the condition monitoring of rotary machines. We inspect the effect of the parameters of DisEn, namely embedding dimension, number of classes, and time delay as well as the length of signals, on its performance to characterize the dynamics of time series. Next, several straight-forward concepts in signal processing using a set of time series are used to show the advantages of DisEn over permutation entropy (PerEn) and approximate entropy (ApEn) in terms of detection of the dynamical variability of signals. The results suggest that DisEn, compared with PerEn and ApEn, leads to more stable results when dealing with a high signal-to-noise-ratio. We also show that DisEn is noticeably faster than ApEn, and thus, it is more appropriate for real-time applications. DisEn is also assessed by three experimental tests for the detection of different gear faults, fault diagnosis of rolling element bearings, and characterization of bearing degradation. The results show that DisEn, compared with PerEn and ApEn, yields more stable results for the status characterization of rotary machines.

Active Hearing Mechanisms Inspire Adaptive Amplification in an Acoustic Sensor System.

Over many millions of years of evolution, nature has developed some of the most adaptable sensors and sensory systems possible, capable of sensing, conditioning and processing signals in a very power- and size-effective manner. By looking into biological sensors and systems as a source of inspiration, this paper presents the study of a bioinspired concept of signal processing at the sensor level. By exploiting a feedback control mechanism between a front-end acoustic receiver and back-end neuronal based computation, a nonlinear amplification with hysteretic behavior is created. Moreover, the transient response of the front-end acoustic receiver can also be controlled and enhanced. A theoretical model is proposed and the concept is prototyped experimentally through an embedded system setup that can provide dynamic adaptations of a sensory system comprising a MEMS microphone placed in a closed-loop feedback system. It faithfully mimics the mosquito's active hearing response as a function of the input sound intensity. This is an adaptive acoustic sensor system concept that can be exploited by sensor and system designers within acoustics and ultrasonic engineering fields.

Gamma ray spectroscopy with Arduino UNO

We review a simple gamma ray spectrometer constructed on a solderless breadboard. The spectrometer's detector consists of a CsI(Tl) scintillator and silicon photomultiplier (SiPM) and its readout is facilitated by an Arduino UNO. The system is low cost and utilizes a minimum of components while still achieving satisfactory charge linearity and noise levels. This instrument can be used in instructional laboratories to introduce both radiation detection and analog signal processing concepts. We also expect it will be of interest to those seeking to introduce gamma spectroscopy to the expanding ecosystem of Arduino hardware.

Matched-Filter Thermography

Conventional infrared thermography techniques, including pulsed and lock-in thermography, have shown great potential for non-destructive evaluation of broad spectrum of materials, spanning from metals to polymers to biological tissues. However, performance of these techniques is often limited due to the diffuse nature of thermal wave fields, resulting in an inherent compromise between inspection depth and depth resolution. Recently, matched-filter thermography has been introduced as a means for overcoming this classic limitation to enable depth-resolved subsurface thermal imaging and improving axial/depth resolution. This paper reviews the basic principles and experimental results of matched-filter thermography: first, mathematical and signal processing concepts related to matched-fileting and pulse compression are discussed. Next, theoretical modeling of thermal-wave responses to matched-filter thermography using two categories of pulse compression techniques (linear frequency modulation and binary phase coding) are reviewed. Key experimental results from literature demonstrating the maintenance of axial resolution while inspecting deep into opaque and turbid media are also presented and discussed. Finally, the concept of thermal coherence tomography for deconvolution of thermal responses of axially superposed sources and creation of depth-selective images in a diffusion-wave field is reviewed.

Optimized dynamic design of laminated piezocomposite multi-entry actuators considering fiber orientation

Laminated piezocomposite actuators (LAPA) are structures composed of piezoelectric and non-piezoelectric materials layers. Due to several parameters and the multiphysics domain involved in the design of LAPA, simple forms are commonly found in industrial applications. However, its design can be systematized by using the topology optimization method (TOM) that permits the solution of complex problems. The design of LAPA with TOM has traditionally considered the optimization of piezoelectric materials over an isotropic substrate, yet some previous researches suggest that LAPA including fiber-reinforced composite layers can increase the performance of these transducers. In addition, works dealing with fiber-based composite focus on static or harmonic analysis with sinusoidal excitations, although other signal inputs are used in practice. In fact, the design of fiber-based LAPA in transient regime has not been assessed before. Thus, a methodology is proposed here to design LAPA with TOM. The actuator is electrically excited with a combined waveform: a sine wave treated as a harmonic problem and a step excitation addressed as a transient problem. Both waves have the same frequency, however they are not applied at the same time. This approach allows the development of a multi-entry actuator since it generates the same level of output displacement independently of the type of excitation input. Consequently, the optimization problem is formulated with the purpose of distributing the material in all layers, the polarization sign in piezoelectric layers and the fiber orientation angle in composite layers, in which the objective function simultaneously seeks for the maximization of the vibration amplitude at certain points of the actuator and its response speed. Eight-node shell elements taking into account the piezoelectric effects are used in the finite element method (FEM) and the “layer wise theory is adopted to model the laminated structure. The Generalized-α method is used to solve the transient problem. In order to optimize the material distribution and the polarization sign, the classical SIMP and PEMAP-P models are used respectively, while to optimize the fiber orientation angles in the composite material, a novel self-penalizable interpolation model is proposed. This optimization problem is solved by using the sequential linear programming (SLP) technique with the CVX solver and the sensitivity analysis is performed with the adjoint method. Discrete signal processing concepts are applied to solve the adjoint problem involving specific points of curves obtained by time integration methods in transient analysis. Numerical techniques are implemented to avoid TOM instabilities. Finally, the potential of this approach is demonstrated with two numerical examples.

A Theoretical Extension of AH FDTD Method and Applications in Various Physical Fields

In this paper, we extended the Associated Hermite (AH) FDTD method theoretically and used it to analyze various physical fields, which are electromagnetic (EM) field, acoustic field, and heat-transfer field. The AH differential transformation operator is developed theoretically as a basic element for setting a time–frequency bridge, where several common operators such as differential, second-order differential, integral linear operator, etc. on the concept of signal processing and analyzing can be expediently transformed, which leads to a unified and much simpler derivation to construct the previous AH FDTD formula. Therefore, it gives a reconsideration for AH FDTD to calculate a more general or even arbitrary liner physic field problems, which are focused on the commonly partial differential equations. Finally, based on the time–frequency bridge theory, we adopted several examples for verification from the formula derivation to numerical validation in EM field, acoustic field, and heat-transfer field, respectively.

Improving Event-Based Non-Intrusive Load Monitoring Using Graph Signal Processing

Large-scale smart energy metering deployment worldwide and integration of smart meters within the smart grid will enable two-way communication between the consumer and energy network, thus ensuring improved response to demand. Energy disaggregation or non-intrusive load monitoring (NILM), namely disaggregation of the total metered electricity consumption down to individual appliances using purely algorithmic tools, is gaining popularity as an added-value that makes the most of meter data. However, NILM remains a challenging problem since NILM is susceptible to sensor noise, unknown load noise, transient spikes, and fluctuations. In this paper, we tackle this problem using novel graph signal processing (GSP) concepts, applied at both, physical signal level via graph-based filtering and data level, via effective semi-supervised GSP-based feature matching. The proposed GSP-based method is generic and can be used to improve results of various event-based NILM approaches. We demonstrate significant improvement in performance using three state-of-the-art NILM methods, both supervised and unsupervised, and real-world active power consumption readings from the REDD and REFIT 1 data sets, sampled at 1 and 8 s, respectively. 1 The REFIT dataset used to generate the results can be accessed via DOI 10.15129/31da3ece-f902-4e95-a093-e0a9536983c4.

Refined generalized multiscale entropy analysis for physiological signals

Multiscale entropy analysis has become a prevalent complexity measurement and been successfully applied in various fields. However, it only takes into account the information of mean values (first moment) in coarse-graining procedure. Then generalized multiscale entropy (MSEn) considering higher moments to coarse-grain a time series was proposed and MSEσ2 has been implemented. However, the MSEσ2 sometimes may yield an imprecise estimation of entropy or undefined entropy, and reduce statistical reliability of sample entropy estimation as scale factor increases. For this purpose, we developed the refined model, RMSEσ2, to improve MSEσ2. Simulations on both white noise and 1∕f noise show that RMSEσ2 provides higher entropy reliability and reduces the occurrence of undefined entropy, especially suitable for short time series. Besides, we discuss the effect on RMSEσ2 analysis from outliers, data loss and other concepts in signal processing. We apply the proposed model to evaluate the complexity of heartbeat interval time series derived from healthy young and elderly subjects, patients with congestive heart failure and patients with atrial fibrillation respectively, compared to several popular complexity metrics. The results demonstrate that RMSEσ2 measured complexity (a) decreases with aging and diseases, and (b) gives significant discrimination between different physiological/pathological states, which may facilitate clinical application.

Quasi-three-dimensional integration scheme using time-domain interconnection

A quasi-three-dimensional integration scheme involving time-domain interconnection (Q3D-TD) is proposed. By utilizing the time space as the third integration dimension, circuit functions can be integrated densely with a quasi-3D interconnect system, resulting in the decrease in critical path delay and the high operation speed of the circuit. As an example of the application of this concept in digital signal processing, multiple-layered 2D image averaging filters (8×8 pixels, 8 bit depth, and 3×3 core) are designed and evaluated. An average speedup of 10.2% is achieved by Q3D-TD for two-layer to eight-layer 2D image filters.

Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis

Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of biomedical time series. Recent developments in the field have tried to alleviate the problem of undefined MSE values for short signals. Moreover, there has been a recent interest in using other statistical moments than the mean, i.e., variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite multiscale fuzzy entropy based on the standard deviation (RCMFEσ) and mean (RCMFEμ) to quantify the dynamical properties of spread and mean, respectively, over multiple time scales. We demonstrate the dependency of the RCMFEσ and RCMFEμ, in comparison with other multiscale approaches, on several straightforward signal processing concepts using a set of synthetic signals. The results evidenced that the RCMFEσ and RCMFEμ values are more stable and reliable than the classical multiscale entropy ones. We also inspect the ability of using the standard deviation as well as the mean in the coarse-graining process using magnetoencephalograms in Alzheimer’s disease and publicly available electroencephalograms recorded from focal and non-focal areas in epilepsy. Our results indicated that when the RCMFEμ cannot distinguish different types of dynamics of a particular time series at some scale factors, the RCMFEσ may do so, and vice versa. The results showed that RCMFEσ-based features lead to higher classification accuracies in comparison with the RCMFEμ-based ones. We also made freely available all the Matlab codes used in this study at http://dx.doi.org/10.7488/ds/1477.

Adaptive acoustic energy delivery to near and far fields using foldable, tessellated star transducers

Methods of guiding acoustic energy arbitrarily through space have long relied on digital controls to meet performance needs. Yet, more recent attention to adaptive structures with unique spatial configurations has motivated mechanical signal processing (MSP) concepts that may not be subjected to the same functional and performance limitations as digital acoustic beamforming counterparts. The periodicity of repeatable structural reconfiguration enabled by origami-inspired tessellated architectures turns attention to foldable platforms as frameworks for MSP development. This research harnesses principles of MSP to study a tessellated, star-shaped acoustic transducer constituent that provides on-demand control of acoustic energy guiding via folding-induced shape reconfiguration. An analytical framework is established to probe the roles of mechanical and acoustic geometry on the far field directivity and near field focusing of sound energy. Following validation by experiments and verification by simulations, parametric studies are undertaken to uncover relations between constituent topology and acoustic energy delivery to arbitrary points in the free field. The adaptations enabled by folding of the star-shaped transducer reveal capability for restricting sound energy to angular regions in the far field while also introducing means to modulate sound energy by three orders-of-magnitude to locations near to the transducer surface. In addition, the modeling philosophy devised here provides a valuable approach to solve general sound radiation problems for foldable, tessellated acoustic transducer constituents of arbitrary geometry.

Extreme nonlinear terahertz electro-optics in diamond for ultrafast pulse switching

Polarization switching of picosecond laser pulses is a fundamental concept in signal processing [C. Chen and G. Liu, Annu. Rev. Mater. Sci. 16, 203 (1986); V. R. Almeida et al., Nature 431, 1081 (2004); and A. A. P. Pohl et al., Photonics Sens. 3, 1 (2013)]. Conventional switching devices rely on the electro-optical Pockels effect and work at radio frequencies. The ensuing gating time of several nanoseconds is a bottleneck for faster switches which is set by the performance of state-of-the-art high-voltage electronics. Here we show that by substituting the electric field of several kV/cm provided by modern electronics by the MV/cm field of a single-cycle THz laser pulse, the electro-optical gating process can be driven orders of magnitude faster, at THz frequencies. In this context, we introduce diamond as an exceptional electro-optical material and demonstrate a pulse gating time as fast as 100 fs using sub-cycle THz-induced Kerr nonlinearity. We show that THz-induced switching in the insulator diamond is fully governed by the THz pulse shape. The presented THz-based electro-optical approach overcomes the bandwidth and switching speed limits of conventional MHz/GHz electronics and establishes the ultrafast electro-optical gating technology for the first time in the THz frequency range. We finally show that the presented THz polarization gating technique is applicable for advanced beam diagnostics. As a first example, we demonstrate tomographic reconstruction of a THz pulse in three dimensions.

A Correlation Study on Motion Artifact using Photodiode and Three Axis Accelerometer Signals

Objectives:The three-axis accelerometer used to reflect the Motion Artifact (MA) implemented in the Adaptive Noise Cancellation (ANC) technique has failed to provide the required MA for accurate processing. This study investigates the validity of using a Covered Photodetector (CPD) for MA detection to unify the sources of generated desired and MA signals to avoid electronic noise differences. Methods/Statistical Analysis:The two devices were fastened together and exposed to various kinds of motions. The Amplitude Fluctuations (AFs) responding in the time domain and the Linear Correlation Coefficient (r) were observed. Findings:Confirmed the proposed approach validity by pointing a rapprochement in AFs. The calculated r was0.999 in asteady state while in motion states rangesfrom 0.998 to 0.794 according to movement changes. Application/Improvements: Implementing such concept for Photoplethysmography signal processing within ANC leads to a curate critical medical diagnosis at a lower cost.

Advances in biomedical signal and image processing – A systematic review

Abstract Biomedical signal and image processing establish a dynamic area of specialization in both academic as well as research aspects of biomedical engineering. The concepts of signal and image processing have been widely used for extracting the physiological information in implementing many clinical procedures for sophisticated medical practices and applications. In this paper, the relationship between electrophysiological signals, i.e., electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and functional image processing and their derived interactions have been discussed. Examples have been investigated in various case studies such as neurosciences, functional imaging, and cardiovascular system, by using different algorithms and methods. The interaction between the extracted information obtained from multiple signals and modalities seems to be very promising. The advanced algorithms and methods in the area of information retrieval based on time-frequency representation have been investigated. Finally, some examples of algorithms have been discussed in which the electrophysiological signals and functional images have been properly extracted and have a significant impact on various biomedical applications.