Genuine Signal Research Articles

Magnetic control of heat generation in a magnetic tunnel contact with spin accumulation

It is described how the heat generation by an electrical current in a ferromagnetic tunnel contact to a nonmagnetic material depends on the spin accumulation in the nonmagnetic electrode for both Joule and Peltier heating. This enables the control of the heating in the tunnel contact by an external applied magnetic field that induces spin precession of the spin accumulation. Thereby, a Hanle type of spin signal is imprinted in the heating power. We derive expressions for the magnitude and sign of the magnetic-field-dependent heating power for Joule and Peltier heating, and discuss the important parameters. For contacts with small resistance-area product, the spin accumulation provides the dominant contribution to the Joule heating power, which can therefore be modulated by more than a factor of 2 with a magnetic field. The described phenomenon can also produce genuine spin signals in various devices, including nonlocal lateral spin valves.

Molecular photothermal effects on time-resolved IR spectroscopy.

Time-resolved IR pump-probe (IR-PP) and two-dimensional IR (2D-IR) spectroscopy are valuable techniques for studying various ultrafast chemical and biological processes in solutions. The time-dependent changes of nonlinear IR signals reflecting fast molecular processes such as vibrational energy transfer and chemical exchange provide invaluable information on the rates and mechanisms of solvation dynamics and structural transitions of multispecies vibrationally interacting molecular systems. However, due to the intrinsic difficulties in distinguishing the contributions of molecule-specific processes to the time-resolved IR signals from those resulting from local heating, it becomes challenging to interpret time-resolved IR-PP and 2D-IR spectra exhibiting transient growing-in spectral components and cross-peaks unambiguously. Here, theoretical considerations of various effects of vibrational coupling, energy transfer, chemical exchange, the generation of hot ground states, molecular photothermal process, and their combinations on the line shapes and time-dependent intensities of IR-PP spectra and 2D-IR diagonal peaks and cross-peaks are presented. We anticipate that the present work will help researchers using IR pump-probe and 2D-IR techniques to distinguish local heating-induced photothermal signals from genuine nonlinear IR signals.

A comparative atlas of selected skeletal elements of European urodeles (Amphibia: Urodela) for palaeontological investigations

AbstractThe osteology of European urodeles was studied extensively in the past, but comparative analyses of isolated bones are rare, despite being the most useful tool for the identification of fossil remains. The present work is focused on the most robust skeletal elements (therefore, common in the fossil record: otic–occipitum complexes and vertebrae) and provides taxonomically significant diagnostic characters at the genus level for European genera of urodeles, including some taxa whose osteology was poorly known (e.g. Euproctus and Calotriton). Characters subject to wide variability are identified, and their use is discouraged for diagnoses of extinct species. A selection of diagnostic characters is used herein to build an identification key for each studied skeletal element and for a phylogenetic analysis to understand whether these osteological elements can convey a genuine signal. The analysis under Bayesian inference resulted in a well-supported Salamandridae clade and Pleurodelinae tribe. Salamandrina was recovered as part of the Pleurodelinae, in agreement with previous morphological phylogenetic analyses, but in contrast to the conclusion of molecular studies.

Comprehensive taxon sampling and vetted fossils help clarify the time tree of shorebirds (Aves, Charadriiformes)

Shorebirds (Charadriiformes) are a globally distributed clade of modern birds and, due to their ecological and morphological disparity, a frequent subject of comparative studies. While molecular phylogenies have been key to establishing the suprafamilial backbone of the charadriiform tree, a number of relationships at both deep and shallow taxonomic levels remain poorly resolved. The timescale of shorebird evolution also remains uncertain as a result of extensive disagreements among the published divergence dating studies, stemming largely from different choices of fossil calibrations. Here, we present the most comprehensive non-supertree phylogeny of shorebirds to date, based on a total-evidence dataset comprising 353 ingroup taxa (90% of all extant or recently extinct species), 27 loci (15 mitochondrial and 12 nuclear), and 69 morphological characters. We further clarify the timeline of charadriiform evolution by time-scaling this phylogeny using a set of 14 up-to-date and thoroughly vetted fossil calibrations. In addition, we assemble a taxonomically restricted 100-locus dataset specifically designed to resolve outstanding problems in higher-level charadriiform phylogeny. In terms of tree topology, our results are largely congruent with previous studies but indicate that some of the conflicts among earlier analyses reflect a genuine signal of pervasive gene tree discordance. Monophyly of the plovers (Charadriidae), the position of the ibisbill (Ibidorhyncha), and the relationships among the five subfamilies of the gulls (Laridae) could not be resolved even with greatly increased locus and taxon sampling. Moreover, several localized regions of uncertainty persist in shallower parts of the tree, including the interrelationships of the true auks (Alcinae) and anarhynchine plovers. Our node-dating and macroevolutionary rate analyses find support for a Paleocene origin of crown-group shorebirds, as well as exceptionally rapid recent radiations of Old World oystercatchers (Haematopodidae) and select genera of gulls. Our study underscores the challenges involved in estimating a comprehensively sampled and carefully calibrated time tree for a diverse avian clade, and highlights areas in need of further research.

New fossils from Kromdraai and Drimolen, South Africa, and their distinctiveness among Paranthropus robustus

Most fossil hominin species are sampled with spatial, temporal or anatomical biases that can hinder assessments of their paleodiversity, and may not yield genuine evolutionary signals. We use new fossils from the Kromdraai (Unit P) and Drimolen sites (South Africa) to provide insights into the paleodiversity of the Lower Pleistocene robust australopith, Paranthropus robustus. Our focus is the morphology of the temporal bone and the relationships between size and shape (allometry) of the semi-circular canals (SCC), an aspect that has not yet been investigated among southern African australopiths. We find significant size and shape SCC differences between P. robustus from Kromdraai, Drimolen and Swartkrans. This site-related variation is consistent with other differences observed on the temporal bone. P. robustus from Kromdraai Unit P is distinctive because of its smaller temporal bone and SCC, and its proportionally less developed posterior SCC, independently of age and sex. We emphasize the importance of allometry to interpret paleodiversity in P. robustus as either the consequence of differences in body size, or as yet unknown factors. Some features of the inner ear of P. robustus represent directional selection soon after its origin, whereas the size and shape variations described here may result from evolutionary changes.

Search for neutrino emission from cores of active galactic nuclei

The sources of the majority of the high-energy astrophysical neutrinos observed with the IceCube neutrino telescope at the South Pole are unknown. So far, only a gamma-ray blazar was compellingly associated with the emission of high-energy neutrinos. In addition, several studies suggest that the neutrino emission from the gamma-ray blazar population only accounts for a small fraction of the total astrophysical neutrino flux. In this work we probe the production of high-energy neutrinos in the cores of Active Galactic Nuclei (AGN), induced by accelerated cosmic rays in the accretion disk region. We present a likelihood analysis based on eight years of IceCube data, searching for a cumulative neutrino signal from three AGN samples created for this work. The neutrino emission is assumed to be proportional to the accretion disk luminosity estimated from the soft X-ray flux. Next to the observed soft X-ray flux, the objects for the three samples have been selected based on their radio emission and infrared color properties. For the largest sample in this search, an excess of high-energy neutrino events with respect to an isotropic background of atmospheric and astrophysical neutrinos is found, corresponding to a post-trial significance of 2.60$\sigma$. Assuming a power-law spectrum, the best-fit spectral index is $2.03^{+0.14}_{-0.11}$, consistent with expectations from particle acceleration in astrophysical sources. If interpreted as a genuine signal with the assumptions of a proportionality of X-ray and neutrino fluxes and a model for the sub-threshold flux distribution, this observation implies that at 100 TeV, 27$\%$ - 100$\%$ of the observed neutrinos arise from particle acceleration in the core of AGN.

Asymptotic Survival of Genuine Multipartite Entanglement in Noisy Quantum Networks Depends on the Topology

The study of entanglement in multipartite quantum states plays a major role in quantum information theory and genuine multipartite entanglement signals one of its strongest forms for applications. However, its characterization for general (mixed) states is a highly nontrivial problem. We introduce a particularly simple subclass of multipartite states, which we term pair-entangled network (PEN) states, as those that can be created by distributing exclusively bipartite entanglement in a connected network. We show that genuine multipartite entanglement in a PEN state depends on both the level of noise and the network topology and, in sharp contrast to the case of pure states, it is not guaranteed by the mere distribution of mixed bipartite entangled states. Our main result is a markedly drastic feature of this phenomenon: the amount of connectivity in the network determines whether genuine multipartite entanglement is robust to noise for any system size or whether it is completely washed out under the slightest form of noise for a sufficiently large number of parties. This latter case implies fundamental limitations for the application of certain networks in realistic scenarios, where the presence of some form of noise is unavoidable. To illustrate the applicability of PEN states to study the complex phenomenology behind multipartite entanglement, we also use them to prove superactivation of genuine multipartite nonlocality for any number of parties.

Fault diagnosis of SKF-6205 bearing with modified empirical mode decomposition

Rolling element bearings are broadly used in the rotating machines to support static and dynamic loads. In this research, the advance signal processing techniques are use for processing of bearing fault signals. Experimental validation with genuine vibration signals calculated from bearings with seeded defects on bearing elements. The model-based fault diagnosis method has attempted to diagnose incipient fault detection and classification of bearing with data driven approach. Feature extraction technique has been developed with hybrid signal processing technique based on the band pass filter nature of Empirical mode decomposition (EMD), the resonant frequency bands have owed in specific mono component signals called Intrinsic Mode Functions (IMFs). Synchronized resonant frequency band (SRFB) is obtained on based of orthogonal real wavelet using spectral kurtosis. Biorthogonal 5.5 wavelet, a real wavelet has been selected as a suitable wavelet for WPT based on “Maximum Relative Wavelet Energy” and “Maximum Energy-to-Shannon entropy ratio”. Three, Feature extraction techniques like continuous wavelet transform (CWT), wavelet packet transform (WPT) and modified Hilbert Huang Transforms (HHT) are compared on bases of their classification accuracy with different classification algorithm and filters. Various supervised classifiers have been compared through a common platform of Waikato Environment for Knowledge Analysis (WEKA) and concluded the k- nearest neighbour (KNN) as an effective available classifier for rolling element bearing. Further, asymmetric proximity function based KNN (APF-KNN) has out performs with modified feature selection criteria. Feature extraction through modified HHT and APFKNN has been future as a most effectual fault classification method. For testing any unknown data, simplified method has been demonstrated, which make the proposed data driven approach more realistic, faster and automated.

Heavy polarons in ultracold atomic Fermi superfluids at the BEC-BCS crossover: Formalism and applications

We investigate the system of a heavy impurity embedded in a paired two-component Fermi gas at the crossover from a Bose-Einstein condensate (BEC) to a Bardeen-Cooper-Schrieffer (BCS) superfluid via an extension of the functional determinant approach (FDA). FDA is an exact numerical approach applied to study manifestations of Anderson's orthogonality catastrophe (OC) in the system of a static impurity immersed in an ideal Fermi gas. Here, we extend the FDA to a strongly correlated superfluid background described by a BCS mean-field wave function. In contrast to the ideal Fermi gas case, the pairing gap in the BCS superfluid prevents the OC and leads to genuine polaron signals in the spectrum. Thus our exactly solvable model can provide a deeper understanding of polaron physics. In addition, we find that the polaron spectrum can be used to measure the superfluid pairing gap, and in the case of a magnetic impurity, the energy of the subgap Yu-Shiba-Rusinov (YSR) bound state. Our theoretical predictions can be examined with state-of-art cold-atom experiments.

Testing modified (Horndeski) gravity by combining intrinsic galaxy alignments with cosmic shear

ABSTRACT We study the impact of modified gravity of the Horndeski class on intrinsic shape correlations in cosmic shear surveys. As these correlations intrinsic alignments are caused by tidal gravitational fields acting on galaxies as a collection of massive non-relativistic test particles, they are only sensitive to the gravitational potential, which forms in conjunction with the curvature perturbation. In contrast, the cosmic shear signal probes the sum of these two, i.e. both Bardeen potentials. Combining these probes therefore constitutes a test of gravity, derived from a single measurement. Focusing on linear scales and alignments of elliptical galaxies, we study the impact on inference of the braiding $\hat{\alpha }_{\rm B}$ and the time evolution of the Planck mass $\hat{\alpha }_{\rm M}$ by treating IAs as a genuine signal contributing to the overall ellipticity correlation. We find that for euclid, IAs can help to improve constraints on modified gravity of the Horndeski-class by 10 per cent if the alignment parameter needed for the linear alignment model is provided by simulations. If, however, the IA needs to be self-calibrated, all of the sensitivity is put into the inference of the alignment strength D since there is a very strong correlation with the evolution of the Planck mass. Thus, diminishing the benefit of IA for probing modified gravitational theories. While this paper shows results mainly for modified gravity parameters, similar deductions can be drawn for the investigation of anisotropic stresses, parametrized modifications to the Poisson equation, the phenomenology of gravitational slip and to breaking degeneracies in a standard cosmology.

Confocal microscopy analysis reveals that only a small proportion of extracellular vesicles are successfully labelled with commonly utilised staining methods

Assessing genuine extracellular vesicle (EV) uptake is crucial for understanding the functional roles of EVs. This study measured the bona fide labelling of EVs utilising two commonly used fluorescent dyes, PKH26 and C5-maleimide-Alexa633. MCF7 EVs tagged with mEmerald-CD81 were isolated from conditioned media by size exclusion chromatography (SEC) and characterised using Nanoparticle Tracking Analysis (NTA), Transmission Electron Microscopy (TEM), MACsPlex immunocapture assay and immunoblots. These fluorescently tagged EVs were subsequently stained with C5-maleimide-Alexa633 or PKH26, according to published protocols. Colocalisation of dual-labelled EVs was assessed by confocal microscopy and quantified using the Rank-Weighted Colocalisation (RWC) algorithm. We observed strikingly poor colocalisation between mEmerald-CD81-tagged EVs and C5-Maleimide-Alexa633 (5.4% ± 1.8) or PKH26 (4.6% ± 1.6), that remained low even when serum was removed from preparations. Our data confirms previous work showing that some dyes form contaminating aggregates. Furthermore, uptake studies showed that maleimide and mEmerald-CD81-tagged EVs can be often located into non-overlapping subcellular locations. By using common methods to isolate and stain EVs we observed that most EVs remained unstained and most dye signal does not appear to be EV associated. Our work shows that there is an urgent need for optimisation and standardisation in how EV researchers use these tools to assess genuine EV signals.

EGHWT: The Extended Generalized Haar\u2013Walsh Transform

Extending computational harmonic analysis tools from the classical setting of regular lattices to the more general setting of graphs and networks is very important, and much research has been done recently. The generalized Haar–Walsh transform (GHWT) developed by Irion and Saito (2014) is a multiscale transform for signals on graphs, which is a generalization of the classical Haar and Walsh–Hadamard transforms. We propose the extended generalized Haar–Walsh transform (eGHWT), which is a generalization of the adapted time–frequency tilings of Thiele and Villemoes (1996). The eGHWT examines not only the efficiency of graph-domain partitions but also that of “sequency-domain” partitions simultaneously. Consequently, the eGHWT and its associated best-basis selection algorithm for graph signals significantly improve the performance of the previous GHWT with the similar computational cost, O(N log N), where N is the number of nodes of an input graph. While the GHWT best-basis algorithm seeks the most suitable orthonormal basis for a given task among more than (1.5)^N possible orthonormal bases in mathbb {R}^N, the eGHWT best-basis algorithm can find a better one by searching through more than 0.618cdot (1.84)^N possible orthonormal bases in mathbb {R}^N. This article describes the details of the eGHWT best-basis algorithm and demonstrates its superiority using several examples including genuine graph signals as well as conventional digital images viewed as graph signals. Furthermore, we also show how the eGHWT can be extended to 2D signals and matrix-form data by viewing them as a tensor product of graphs generated from their columns and rows and demonstrate its effectiveness on applications such as image approximation.

A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors

In this paper, we propose a BPR-CNN (Biometric Pattern Recognition-Convolution Neural Network) classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction by EF (Electric Field) sensors. Currently, an EF sensor or EPS (Electric Potential Sensor) system is attracting attention as a next-generation motion sensing technology due to low computation and price, high sensitivity and recognition speed compared to other sensor systems. However, it remains as a challenging problem to accurately detect and locate the authentic motion signal frame automatically in real-time when sensing body-motions such as hand motion, due to the variance of the electric-charge state by heterogeneous surroundings and operational conditions. This hinders the further utilization of the EF sensing; thus, it is critical to design the robust and credible methodology for detecting and extracting signals derived from the motion movement in order to make use and apply the EF sensor technology to electric consumer products such as mobile devices. In this study, we propose a motion detection algorithm using a dynamic offset-threshold method to overcome uncertainty in the initial electrostatic charge state of the sensor affected by a user and the surrounding environment of the subject. This method is designed to detect hand motions and extract its genuine motion signal frame successfully with high accuracy. After setting motion frames, we normalize the signals and then apply them to our proposed BPR-CNN motion classifier to recognize their motion types. Conducted experiment and analysis show that our proposed dynamic threshold method combined with a BPR-CNN classifier can detect the hand motions and extract the actual frames effectively with 97.1% accuracy, 99.25% detection rate, 98.4% motion frame matching rate and 97.7% detection & extraction success rate.

Learning Temporal Similarity of Remote Photoplethysmography for Fast 3D Mask Face Presentation Attack Detection

To detect 3D mask face presentation attack, remote Photoplethysmography (rPPG), a biomedical technique that measures the heartbeat signal remotely with a normal RGB camera, is adopted as a robust liveness cue. Although existing rPPG-based solutions exhibit strong performance in experiments, the required observation time is too long (10-12 seconds) to be user-friendly in real applications such as E-payment and smartphone unlock. To shorten the observation time (within 1-second), we propose a fast rPPG-based 3D mask presentation attack detection (PAD) method by analyzing the similarity of rPPG signals in the time domain. In particular, based on facial and background local rPPG signals, we design a set of temporal similarity features to investigate the robust properties of rPPG shape and phase. Following the same direction, we refine the traditional rPPG extractor into a learnable network to cooperate with our TSrPPG feature for better robustness. An effective but lightweight spatiotemporal convolution network is constructed with a self-supervised learning strategy, aiming at enhancing the consistency of genuine facial rPPG signals and reducing the correlation of rPPG signals on masked faces. Extensive experiments are conducted on 3DMAD, HKBU-MARs V1+ and V2+, and CSMAD, which totally involve 18772 short-term video slots with a large number of real-world variations, in terms of mask type, mask transmittance, lighting condition, recording device, resolution of facial region, and compression configuration. Our proposed method persists the good performance of rPPG-based solution with only 1-second observation and outperforms the state-of-the-art competitors on discriminability and generalizability. Evaluations on prints attack, display attack, and disguise attacks with transparent masks, make-up and tattoo further exhibit its potential on handling a wider variety of attacks. To our best knowledge, this is the first work that addresses the length of observation time issue of rPPG-based 3D mask PAD.

Bayesian multistudy factor analysis for high-throughput biological data

This paper analyzes breast cancer gene expression across seven studies to identify genuine and thus replicable gene patterns shared among these studies. Our premise is that genuine biological signal is more likely to be reproducibly present in multiple studies than spurious signal. Our analysis uses a new modeling strategy for the joint analysis of high-throughput biological studies which simultaneously identifies shared as well as study-specific signal. To this end, we generalize the multi-study factor analysis model to handle high-dimensional data and generalize the sparse Bayesian infinite factor model to this context. We provide strategies for the identification of the loading matrices, common and study-specific. Through extensive simulation analysis, we characterize the performance of the proposed approach in various scenarios and show that it outperforms standard factor analysis in identifying replicable signal in all scenarios considered. The analysis of breast cancer gene expression studies identifies clear replicable gene patterns. These patterns are related to well-known biological pathways involved in breast cancer, such as the ER, cell cycle, immune system, collagen, and metabolic pathways. Some of these patterns are also associated with existing breast cancer subtypes, such as LumA, Her2, and basal subtypes, while other patterns identify novel pathways active across subtypes and missed by hierarchical clustering approaches. The R package MSFA implementing the method is available on GitHub.

Automatic classification of ICA components from infant EEG using MARA

Automated systems for identifying and removing non-neural ICA components are growing in popularity among EEG researchers of adult populations. Infant EEG data differs in many ways from adult EEG data, but there exists almost no specific system for automated classification of source components from paediatric populations. Here, we adapt one of the most popular systems for adult ICA component classification for use with infant EEG data. Our adapted classifier significantly outperformed the original adult classifier on samples of naturalistic free play EEG data recorded from 10 to 12-month-old infants, achieving agreement rates with the manual classification of over 75% across two validation studies (n = 44, n = 25). Additionally, we examined both classifiers’ ability to remove stereotyped ocular artifact from a basic visual processing ERP dataset compared to manual ICA data cleaning. Here, the new classifier performed on level with expert manual cleaning and was again significantly better than the adult classifier at removing artifact whilst retaining a greater amount of genuine neural signal operationalised through comparing ERP activations in time and space. Our new system (iMARA) offers developmental EEG researchers a flexible tool for automatic identification and removal of artifactual ICA components.

The Exposome Paradigm to Understand the Environmental Origins of Mental Disorders.

There is an active interest in understanding the relationship between mental disorders and modifiable and potentially preventable exposures. However, the complexity of the environment, involving many causal and noncausal pathways, makes research extremely challenging. To tackle these challenges, we have recently proposed the use of the exposome paradigm. The exposome represents the totality of exposures in a lifetime from conception onward. The framework offers a solution to handle the complexity of all "non-genetic" factors. The exposome approach has recently been adopted to construct an exposome score for schizophrenia (ES-SCZ). Findings demonstrate that ES-SCZ can be used for risk stratification, adjusting for cumulative environmental load in statistical testing, and collecting risk enriched cohorts. Increasing data availability will help improve ES-SCZ that can be used in staging models to enhance clinical characterization and outcome forecasting. Although an ES-SCZ already provides several practical benefits for research practice, the exposome paradigm offers much more. Agnostic exposure-wide analyses might be the first step to mapping the exposome of mental disorders. These analyses help distinguish genuine signals from selective reporting and uncover novel risk and resilience factors. The exposome approach will also increase our understanding of the differential impact of the environment on mental health across geographical settings and ethnic communities. We are in the early phases of exposome research in psychiatry; however, if successfully applied, exposome framework is poised to embrace complexity and enable advanced analytical solutions to harness ever-growing data to gain insight into the complex dynamic network of exposures.

HADES RV programme with HARPS-N at TNG

Context. It is now well-established that small, rocky planets are common around low-mass stars. However, the detection of such planets is challenged by the short-term activity of host stars. Aims. The HARPS-N red Dwarf Exoplanet Survey programme is a long-term project at the Telescopio Nazionale Galileo aimed at monitoring nearby, early-type, M dwarfs, using the HARPS-N spectrograph to search for small, rocky planets. Methods. A total of 174 HARPS-N spectroscopic observations of the M0.5V-type star GJ 9689 taken over the past seven years have been analysed. We combined these data with photometric measurements to disentangle signals related to the stellar activity of the star from possible Keplerian signals in the radial velocity data. We ran an MCMC analysis, applying Gaussian process regression techniques to model the signals present in the data. Results. We identify two periodic signals in the radial velocity time series, with periods of 18.27 and 39.31 d. The analysis of the activity indexes, photometric data, and wavelength dependency of the signals reveals that the 39.31 d signal corresponds to the stellar rotation period. On the other hand, the 18.27 d signal shows no relation to any activity proxy or the first harmonic of the rotation period. We, therefore, identify it as a genuine Keplerian signal. The best-fit model describing the newly found planet, GJ 9689 b, corresponds to an orbital period of Pb = 18.27 ± 0.01 d and a minimum mass of MP sini = 9.65 ± 1.41 M⊕.

Authenticated Smart Meter Reading for a Secure Smart Grid

A secure system is proposed in order to authenticate smart meter reading for securing the overall smart grid communication. Compression based techniques are employed to prevent power reading by unauthenticated entities. Impersonation attacks are common in smart grid environment which cause drastic security issues along with economic issues. Compression along with authentication is studied here to weed out the potential threats raised by unauthenticated access. Hypothesis based testing analysis is done to differentiate intruder based signal and genuine signal. This could be applied to meter data more effectively and is thought to bring about low authentication issues concerned with smart meter data. So the increased threat level in smart meter communications in grid can be lowered efficiently by using compression and authentication and make the overall system secure.

ESREEM: Efficient Short Reads Error Estimation Computational Model for Next-generation Genome Sequencing

Aims: To assess the error profile in NGS data, generated from high throughput sequencing machines. Background: Short-read sequencing data from Next Generation Sequencing (NGS) are currently being generated by a number of research projects. Depicting the errors produced by NGS platforms and expressing accurate genetic variation from reads are two inter-dependent phases. It has high significance in various analyses, such as genome sequence assembly, SNPs calling, evolutionary studies, and haplotype inference. The systematic and random errors show incidence profile for each of the sequencing platforms i.e. Illumina sequencing, Pacific Biosciences, 454 pyrosequencing, Complete Genomics DNA nanoball sequencing, Ion Torrent sequencing, and Oxford Nanopore sequencing. Advances in NGS deliver galactic data with the addition of errors. Some ratio of these errors may emulate genuine true biological signals i.e., mutation, and may subsequently negate the results. Various independent applications have been proposed to correct the sequencing errors. Systematic analysis of these algorithms shows that state-of-the-art models are missing. Objective: In this paper, an effcient error estimation computational model called ESREEM is proposed to assess the error rates in NGS data. Methods: The proposed model prospects the analysis that there exists a true linear regression association between the number of reads containing errors and the number of reads sequenced. The model is based on a probabilistic error model integrated with the Hidden Markov Model (HMM). Result: The proposed model is evaluated on several benchmark datasets and the results obtained are compared with state-of-the-art algorithms. Conclusions: Experimental results analyses show that the proposed model efficiently estimates errors and runs in less time as compared to others.