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Since its discovery in 1909, the Moho was routinely studied by seismological methods. However, from the 1950s, a possible alternative was introduced by gravimetric inversion. Thanks to satellite gravity missions launched from the beginning of the 21st century, a global inversion became feasible, e.g., leading to the computation of the GEMMA model in 2012. This model was computed inverting the GOCE second radial derivatives of the anomalous potential by a Wiener filter, which was applied in the spherical harmonic domain, considering a two-layer model with lateral and vertical density variations. Moreover, seismic information was introduced in the inversion to deal with the joint estimation/correction of both density and geometry of the crustal model. This study aims at revising the GEMMA algorithm from the theoretical point of view, introducing a cleaner formalization and studying the used approximations more thoroughly. The updates are on: (1) the management of the approximations due to the forward operator linearization required for the inversion; (2) the regularization of spherical harmonic coefficients in the inversion by proper modelling the Moho signal and the gravity error covariances; (3) the inclusion of additional parameters and their regularization in the Least Squares adjustment to correct the density model by exploiting seismic information. Thanks to these updates, a significant improvement from the computational point of view is achieved too, thus the convergence of the iterative solution and the differences with respect to the previous algorithm can be assessed by closed-loop tests, showing the algorithm performance in retrieving the simulated “true” Moho.

The automatic segmentation of the tumor region from Magnetic Resonance cerebrum imageries is a difficult task in medical image analysis. Numerous techniques have been created with the goal of improving the segmentation effectiveness of the automated framework. As of late, Convolutional Neural Networks have accomplished better performance in various recognition tasks. In this paper, 2D-ConvNet with skull stripping (SS-2D ConvNet) based brain tumor segmentation technique have been presented. In the proposed method, initially, the input MRI images are preprocessed to reduce noise and skull stripped to correct the contrast and non-uniformity. It is further processed through the 2D-ConvNet for the segmentation of brain tumor. In particular, the proposed method has been compared with other existing methods, and it achieves better performances and yield precise segmentation with dice scores of 91%, accuracy of 89%, specificity of 98%, and sensitivity of 87%. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to develop a methodology for brain tumor segmentation. Nowadays, brain tumor recognition and fragmentation is one among the pivotal procedure in surgical and medication planning arrangements. It is difficult to segment the tumor area from MRI images due to inaccessibility of edge and appropriately visible boundaries. In this paper, a combination of Artificial Neural Network and Fuzzy K-means algorithm has been presented to segment the tumor locale. It contains four phases, (1) Noise evacuation (2) Attribute extraction and selection (3) Classification and (4) Segmentation. Initially, the procured image is denoised utilizing wiener filter, and then the significant GLCM attributes are extricated from the images. Then Deep Learning based classification has been performed to classify the abnormal images from the normal images. Finally, it is processed through the Fuzzy K-Means algorithm to segment the tumor region separately. This proposed segmentation approach has been verified on BRATS dataset and produces the accuracy of 94%, sensitivity of 98% specificity of 99%, Jaccard index of 96%. The overall accuracy of this proposed technique has been improved by 8% when compared with K-Nearest Neighbor methodology. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a fundamental task in computer vision and image scenery detection. Many applications, such as urban planning, change detection, and environmental monitoring require accurate segmentation. Hence, most segmentation tasks are performed by humans. Currently, with the growth of deep convolutional neural network (DCNN), there are many works aimed to find the best network architecture fitting for this task. In this work, the GoogLeNet classifier is used to perform better segmentation as well as a classification for satellite images. The Wiener filter is used here for image denoising. Data Augmentation is performed to extract high information about the input picture. The output of the above steps helps in classification i.e. it identifies the scenery of the input image with four labels. The result shows that the GoogLeNet based image classification has reduced error rate and it also increases the accuracy of output. Additionally, the efficiency of the Wiener filters also described clearly in the result. [ABSTRACT FROM AUTHOR]

Digital images used in the field of ophthalmology are among the most important methods for automatic detection of certain eye diseases. These processes include image enhancement as a primary step to assist optometrists in identifying diseases. Therefore, many algorithms and methods have been developed for the enhancement of retinal fundus images, which may experience challenges that typically accompany enhancement processes, such as artificial borders and dim lighting that mask image details. To eliminate these problems, a new algorithm is proposed in this paper based on separating colour images into three channels (red, green, and blue). The green channel is passed through a Wiener filter and reinforced using the CLAHE technique before merging with the original red and blue channels. Reducing the green channel noise with this approach is proven effective over the other colour channels. Results from the Contrast Improvement Index (CII) and linear index of fuzziness (r) test indicate the success of the proposed algorithm compared with alternate algorithms in the application of improving blood vessel imagery and other details within ten test fundus images selected from the DRIVER database. [ABSTRACT FROM AUTHOR]

Speech signals are usually affected by noises during the communication process. For suppressing the noise signal that is combined with the speech signal, a Wiener filter is adapted in digital hearing aids. Weiner filter plays an important role in noise suppression and enhancement by estimating the relation between the power spectra of the noise-affected speech signal and the noise signal. Power consumption and the hardware requirement are the important problems in adapting Weiner filter for major communication systems. In this work, we implemented an efficient Wiener filter and applied it for noise suppression along with a real-valued fast Fourier transform (FFT)/real-valued inverse FFT processor in digital hearing aids. The pipelined process was adopted for increasing the performance of the system. The proposed Wiener filter was designed to remove the iteration problems in the conventional Wiener filter. The division operation was replaced by an efficient inverse and multiplication operation in the proposed design. A modified architecture for matrix inversion with low computation complexity was implemented. The complete design computation was based on IEEE-754 standard single-precision floating-point numbers. The Wiener filter and the whole system architecture was implemented and designed on a Field Programmable Gate Array platform and simulated to validate the results in Xilinx ISE tools. An efficient reduction in power and area was obtained by adapting the proposed method for speech signal noise degradation. The performance of the proposed design was found to be 50.01% more efficient than that of existing designs.

This paper addresses the reduced-dimension Wiener filter based on perpendicular partition. Two algorithms, i.e., cyclic per-weight (CPW) and batch CPW (BCPW) are presented, which can efficiently perform reduced-dimension adaptive beamforming. In particular, the CPW is suitable for the slow changing environments that have large snapshots to train the weight vector. The BCPW is designed for fast changing scenarios that have low training sequences and sometimes the number of snapshots is less than the number of sensors. In these two algorithms, the solutions of the weight vectors are circularly solved one by one, which only refer to scalar optimization problem then the matrix inversion is avoided. There is no intermediate transformation or orthogonal/non-orthogonal decomposition requirement in our algorithms, so their implementations are relatively simple. Convergence analysis and the computational complexities comparison are provided. Simulation results show the superiorities of the proposed algorithms. Specifically, the CPW has better convergence properties than previous schemes, and the BCPW has low computational complexity and good output SINR performance in low snapshots scenarios.

Adaptive noise cancellation is a useful linear technique to attenuate unwanted background noise that cannot be removed using traditional frequency-selective filters. Usually, this is due to the signal and noise co-existing in the same frequency band. This paper tests a weighted least mean squares (WLMS) algorithm on a stethoscope system for use in detecting coronary artery disease in the presence of background noise. Each stethoscope is equipped with two microphones: one used to detect heart signals and one used to detect background noise. The WLMS method was used for four different sources of background noise whilst measuring a heartbeat, including a single tone, multiple tones, hospital/clinic noise, and breathing noise. The magnitude-squared coherence between both microphones was unity for the tone scenarios, resulting in complete attenuation. For the other background noise sources, a less-than-unity magnitude-squared coherence resulted in minor and no attenuation. Thus, the coherence function is a tool that can be used to predict the amount of attenuation achievable by linear adaptive noise-cancellation techniques, such as WLMS, as presented in this article.

This work describes a new scheme for denoising noisy electrocardiogram (ECG) signals. In the first step, the noise variance is estimated using the well-known DONOHO's estimator followed by the wavelet-based baseline wander removing. In the second step, the estimated variance is employed in the adaptive 1D Wiener filter to reduce the additive noise. Next, a Low Pass filter, based on the FFT, is applied on the resulting denoised signal. Furthermore, a cascaded Savitzky–Golay (SG) smoother filter is applied to refine the restoration process. The final step consists in the recovering of the R -peaks and the surrounding areas. It can be reported that the suggested algorithm is optimal for the additive Gaussian noise and is useful for other types of noises. Both qualitative and quantitative results, achieved from several experimental tests, establish high-quality restoration ability and the efficiency of the proposed method. Thus, when compared to some powerful techniques recently published, the designed algorithm demonstrates very competitive performances. [ABSTRACT FROM AUTHOR]

The paper presents an improvement of the watermark extraction in speech signal watermarking. The noise added to the speech signal during transmission affects the efficiency of the watermark extraction. Removing this noise may aid to enhance the extraction process. There are methods of the speech signal enhancement, which aims to reduce the noise distortion in the speech signal. In this paper, the watermark process is done using the hybrid strategy of the Empirical Mode Decomposition (EMD) and the block-based Singular Value Decomposition (block-based SVD) with chaotic encrypted watermark. The watermark is embedded into the Singular Values matrix (SVs) because of its stability against any disturbance in the speech signal due to the different attacks. The encrypted watermark increases the security level of the watermark. When the watermark extracted at the receiver side, the speech signal will be enhanced first using spectral subtraction, Wiener filter or adaptive Wiener filter enhancement methods. The paper introduces a comparison study to evaluate the performance of each of them. Simulation results indicate that using the enhancement step improve the watermark extraction, especially using the adaptive Wiener filter. [ABSTRACT FROM AUTHOR]

Pre-term birth is the birth that carries out before the baby's expected date. Sometimes, it augments the possibility of health problems or death. In this research, the abdominal EHG signal incorporates both the maternal heart beat signal and Fetal ECG signal. The removal of fetal ECG signal from the heart beat signal is difficult in pre-term detection. In this work, the EHG signal is pre-processed using wiener filter that is applied to enhance the signal quality. Then, the attributes are removed from the pre-processed signal to find the distinctive class. In addition, Opposition based Ant Lion Optimization is used to select the multi-kernel Support Vector Machine and training algorithms for predicting the pre-term birth. The proposed methodology is simulated by using MATLAB software and the results are investigated to verify the classification accuracy. From the experimental study, the proposed work enhanced the classification accuracy upto 3–19% related to the existing works. [ABSTRACT FROM AUTHOR]

Multi-modality image fusion is an effective technique for fusing complementary information from multi-modality images into an integrated image. The additional information obtained from fused images not only enhances visibility to the human eye, but also complements the limitations of each image. The existing image fusion techniques lacks in preserving the quality of image. Therefore to preserve the structure information and perform the detailed information of source images, a novel image fusion scheme based on Multi-Scale Singular Value Decomposition (MSVD) is proposed. Initially, source images are decomposed into detailed and smooth layer by means of MSVD. Then low- and high-frequency components are extracted with the help of proposed filtering technique and subtraction rule. For efficiently selecting appropriate components, Optimisation algorithm is performed by maximising the mutual information of images. Finally, based on additive fusion rule, the fused images are reconstructed through inverse of MSVD approach. The efficiency of the proposed fusion rule is determined by the comparative analysis with existing image fusion method in terms of Mutual Information (MI), Standard Deviation, Entropy, Correlation etc. The experimental outcome shows the superiority of proposed method and it outperforms state-of-the-art methods by providing better quality image with good clarity. Furthermore, the fused image illustrates better visual effect and edge consistency. [ABSTRACT FROM AUTHOR]

Filtered backprojection is an image reconstruction technique for Compton imaging that provides reasonably high resolution at much lower computational costs when compared to iterative methods. This work applies a Wiener filter that has been derived for spherical harmonics on Compton imaging using the OrionUM pixelated CdZnTe imaging spectrometer. To regularize the filter, an investigation is made into the power spectral density of the signal and noise to develop an appropriate spectral signal-to-noise ratio model for the restoration process. Experimental measurements were conducted with two 228 Th sources placed 30° apart. The resulting filtered image of the two sources has an average full-width-at-half-maximum (FWHM) of 9.8° or 7.5° when using a mean squared error and structural similarity optimization approach, respectively, an improvement from the 29.0° FWHM image when using simple backprojection. [ABSTRACT FROM AUTHOR]

Aim: Image is the most powerful tool to analyze the information. Sometimes the captured image gets affected with blur and noise in the environment, which degrades the quality of the image. Image restoration is a technique in image processing where the degraded image can be restored or recovered to its nearest original image. Materials and Methods: In this research Lucy-Richardson algorithm is used for restoring blurred and noisy images using MATLAB software. And the proposed work is compared with Wiener filter, and the sample size for each group is 30. Results: The performance was compared based on three parameters, Power Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Correlation (NC). High values of PSNR, SSIM and NC indicate the better performance of restoration algorithms. Lucy-Richardson provides a mean PSNR of 10.4086db, mean SSIM of 0.4173%, and NC of 0.7433% and Wiener filter provides a mean PSNR of 6.3979db, SSIM of 0.3016%, NC of 0.3276%. Conclusion: Based on the experimental results and statistical analysis using independent sample T test, image restoration using Lucy-Richardson algorithm significantly performs better than Wiener filter on restoring the degraded image with PSNR (P<0.001) and SSIM (P<0.001). [ABSTRACT FROM AUTHOR]

Holographic representations of data enable distributed storage with progressive refinement when the stored packets of data are made available in any arbitrary order. In this paper, we propose and test patch-based transform coding holographic sensing of image data. Our proposal is optimized for progressive recovery under random order of retrieval of the stored data. The coding of the image patches relies on the design of distributed projections ensuring best image recovery, in terms of the l2 norm, at each retrieval stage. The performance depends only on the number of data packets that have been retrieved thus far. Several possible options to enhance the quality of the recovery while changing the size and number of data packets are discussed and tested. This leads us to examine several interesting bit-allocation and rate-distortion trade-offs, highlighted for a set of natural images with ensemble estimated statistical properties. [ABSTRACT FROM AUTHOR]

Fault detection in an induction motor, particularly at premature stage has become necessary to avoid unexpected damage in industrial process. In this paper, an approach to detect the early stage faults in induction machine using motor current signature analysis (MCSA) is presented. It is proposed to estimate the fault severity from stator current using noise cancelation by an adaptive filter (Wiener filter). Wavelet De-noising technique is implemented to reduce the effect of noise floor in noise canceled stator current. Different categories of bearing faults, broken rotor fault and stator inter turn faults in induction motor are estimated with and without de-nosing using pre-fault component cancelation (Noise cancelation). In addition, fault index based on standard deviation (SD) and simple square integral (SSI) value of noise canceled stator current are proposed. The proposed fault detection topology is examined using simulations and experiments on a 3HP, 1HP and 0.5HP induction motors for bearing, broken rotor and stator inter turn faults respectively. [ABSTRACT FROM AUTHOR]

In this article, a statistical channel model is proposed to characterize the Underwater Wireless Optical Communication (UWOC) system in the presence of different air bubbles population considering freshwater scenario. The irradiance fluctuations of the received optical signal are characterized by Gaussian Mixture Model based upon the experimental data. The behavior of the UWOC channel is modeled in the form of an analytical expression. The goodness of fit test shows excellent fit of analytical expression with the experimental measured data under given channel conditions. In order to test the impact of channel characteristics of the practical application, an image is transmitted through UWOC channel in the presence of imperfections due to presence of bubbles. The performance is evaluated in terms of Structure Similarity Index attributed to image quality. A comparative performance analysis of different filtration schemes is also performed in order to improve the performance deterioration due to imperfect channel conditions. A significant improvement of UWOC system using median filter is observed under given channel conditions compared to other cases. [ABSTRACT FROM AUTHOR]

Classification of paddy crop diseases in prior knowledge is the current challenging task to evolve the economic growth of the country. In image processing techniques, the initial process is to eliminate the noise present in the dataset. Removing the noise leads to improvements in the quality of the image. Noise can be removed by applying filtering techniques. In this paper, a novel data repository created from different paddy areas in Vellore, which includes the following diseases, namely Bacteria Leaf Blight, Blast, Leaf Spot, Leaf Holder, Hispa and Healthy leaves. In the initial process, three kinds of noises, namely Salt and Pepper noise, Speckle noise, and Poisson noises, were removed using noise filtering techniques, namely Median and Wiener filter. The interpretation made over the median and Wiener filtering techniques concerning noises, the performance of the methods measured using metrics namely PSNR (peak to signal to noise ration), MSE (mean square error), Maxerr (Maximum squared error), L2rat (ratio of squared error). It is observed that the PSNR value of the hybrid approach is 18.42dB, which produces less error rate as compared with the traditional approach. Results suggest that the methods used in this paper are suitable for processing noise. [ABSTRACT FROM AUTHOR]

Abstract Image denoising is considered a salient pre-processing step in sophisticated imaging applications. Over the decades, numerous studies have been conducted in denoising. Recently proposed Block matching and 3D (BM3D) filtering added a new dimension to the study of denoising. BM3D is the current state-of-the-art of denoising and is capable of achieving better denoising as compared to any other existing method. However, there is room to improve BM3D to achieve high-quality denoising. In this study, to improve BM3D, we first attempted to improve the Wiener filter (the core of BM3D) by maximizing the structural similarity (SSIM) between the true and the estimated image, instead of minimizing the mean square error (MSE) between them. Moreover, for the DC-only BM3D profile, we introduced a 3D zigzag thresholding. Experimental results demonstrate that regardless of the type of the image, our proposed method achieves better denoising performance than that of BM3D.

Existing haze removal approaches have unreliable estimation of atmospheric light and transmission, which lead to imprecision color information recovery, gradient reversal artifacts, and halo effect. In this paper, a novel haze removal technique based on ambient light model is presented. Our technique applies canny edge detection algorithm which can locate the sky area accurately, and adopts binary-tree algorithm to obtain the radiance of atmospheric light A rapidly; then, an adaptive wiener filter is employed for modifying transmission map, and halo effect is further restricted in the sudden changed region of scene depth by the morphology method. Experimental results verify that the optimized approach has better robustness, and is superior to present methods.

The massive multiple-input-multiple-output (MIMO) technology relies on many antennas at base stations to offer high throughput to meet 5G network requirements. However, each base station requires accurate channel state information during channel estimation. Pilot contamination is one of the significant challenges that limit higher order MIMO deployment because it causes channel estimation error. In this paper, a Wiener predictor (WP) using the temporal-based prediction technique was first proposed and then extended as the coordinated WP (CoWP). The WP method is based on prediction using information that has been stored and processed at the base station from a modified subframe, whereas the extended CoWP method requires neighboring base station coordination to assign collection and prediction with other base stations. Results showed a reduction in channel estimation error due to pilot contamination after the Wiener-based prediction and coordinated base station techniques were used. The proposed CoWP technique can increase the performance of channel estimation error by up to 25 and 30 dB relative to the performance of other research technique and conventional MMSE channel estimation. This paper indicates that the pilot contamination effect can be minimized by exploiting the temporal dimension, and this approach increases the number of MIMO order for future 5G base stations, which currently limited due to pilot contamination.

Analytic expressions are derived for the Wiener filter (WF), also known as the linear minimum mean square error (LMMSE) estimator, for an intensity-modulation/direct-detection (IM/DD) short-haul fiber-optic communication system. The link is purely dispersive and the nonlinear square-law detector (SLD) operates at the thermal noise limit. The achievable rates of geometrically shaped PAM constellations are substantially increased by taking the SLD into account as compared to a WF that ignores the SLD. [ABSTRACT FROM AUTHOR]

The decomposition of multi‐component micro‐Doppler signals overlapping in the time‐frequency (T‐F) domain is critical and challenging, especially in the case of irregular instantaneous Doppler frequency. The authors propose a novel time‐based decomposition method called the short‐time variational mode decomposition (STVMD) to analyse the irregular (FM) micro‐Doppler signals, and present an optimal model combined with T‐F transformation. Then, considering the STVMD may fail to extract the instantaneous frequency (IF) of overlapped components, an improved STVMD algorithm is put forward. Since the dependence of the STVMD algorithm on the initial value, they adopt the Kalman filtering to implement IF tracking and regrouping under the global constraint, further accelerating the convergence of the algorithm. Furthermore, due to the mode aliasing at the intersection point, they adopt a degenerate STVMD model to decompose the signals with known centre frequencies, which can be viewed as a Wiener filter. With the two steps, the improved STVMD algorithm can effectively solve the decomposition of T‐F overlapping irregular FM micro‐Doppler signals. Compared with the peak ridge technique and the ridge path regrouping and intrinsic chirp component decomposition (RPRG + ICCD), the proposed method shows the effectiveness and adaptability even for irregular FM signals with large T‐F spectrum amplitude fluctuation in the low signal‐to‐noise ratio environment. [ABSTRACT FROM AUTHOR]

The Moho surface, namely the density discontinuity between crust and mantle, is traditionally studied by seismic methods. However, gravity information can contribute to the Moho estimation and, more generally, to the crustal modeling. The contribution is twofold. First, gravimetry generally provides observations with much lower errors than those implied by the mass density uncertainty and other geophysical assumptions. This means that it can be used to validate existing Moho and/or crustal models by forward modeling. Second, gravity inversion is able to provide diffused (not localized) information on the mass distribution, both regionally and globally (thanks to dedicated satellite gravity missions). However, this information is weak due to its intrinsic ill-posedness. This means that it can be used to correct and spatially interpolate existing models, and to complement seismic, magnetic and geological information to create new models. In this work, the problem of estimating the Moho surface by gravity inversion assuming a two-layer model with lateral and vertical density variations is treated at a regional level. The approach consists in linearizing the forward modeling around a reference Moho at a constant depth and then inverting it through a Wiener filter. This is standard in case of two layers with homogeneous density distributions (or with lateral density variations), while it requires some additional considerations and algorithm modifications in case of vertical density variations. The basic idea is to "condensate" the masses inside the Moho undulation on the reference surface used for the linearization, thus requiring the setup of an iterative procedure. A strategy to introduce seismic information into this inversion procedure is proposed too, with the aim of improving the a priori density modeling. A closed loop test is presented for the algorithm assessment, showing the improvement with respect to a standard approach and the capability of the proposed algorithm to reconstruct the originally simulated Moho undulation by also fitting the gravity and seismic data at a level that is consistent with their observation noise. [ABSTRACT FROM AUTHOR]

Effective diagnosis of skin tumours mainly relies on the analysis of the characteristics of the lesion. Automatic detection of malignant skin lesion has become a mandatory task to reduce the risk of human deaths and increase their survival. This article proposes a study of skin lesion classification using transfer learning approach. The transfer learning model uses four different state-of-the-art architectures, namely Inception v3, Residual Networks (ResNet 50), Dense Convolutional Networks (DenseNet 201) and Inception Residual Networks (Inception ResNet v2). These models are trained under the dataset comprising seven different classes of skin lesions. The skin lesion images are pre-processed using image quantization, grayscaling and the Wiener filter before final training step. These models are compared for performance evaluation on different metrics. The present study shows the efficacy of the methodology for automated classification of lesion images. [ABSTRACT FROM AUTHOR]

Speech enhancement primarily focuses on improving the intelligibility and quality of the speech signal by using various algorithms and techniques. Processing of a speech signal refers to applying efficient mechanisms to reduce noise in the way of extracting the intended speech signal from the corrupted signal. Noise reduction techniques such as kalman filtering, spectral subtraction and adaptive wiener filtering etc. are used in different enhancement scenarios in speech processing. In the proposed method, the combination of wiener filter and Karhunen–Loéve Transform is used to remove noise and enhance the noisy speech signal. This paper presents the performance evaluation of the proposed hybrid algorithm by estimating Signal to Noise Ratio, Perceptual Evaluation of Speech Quality, Short-Time Objective Intelligibility and Extended STOI values. This algorithm has been implemented in varied noisy conditions and the results proved the fruitfulness of this method. Subjective listening evaluation is also done and both the objective and subjective results confirmed the significant improvement in speech intelligibility in the proposed method. [ABSTRACT FROM AUTHOR]

Neural implants that deliver multi-site electrical stimulation to the nervous systems are no longer the last resort but routine treatment options for various neurological disorders. Multi-site electrical stimulation is also widely used to study nervous system function and neural circuit transformations. These technologies increasingly demand dynamic electrical stimulation and closed-loop feedback control for real-time assessment of neural function, which is technically challenging since stimulus-evoked artifacts overwhelm the small neural signals of interest. We report a novel and versatile artifact removal method that can be applied in a variety of settings, from single- to multi-site stimulation and recording and for current waveforms of arbitrary shape and size. The method capitalizes on linear electrical coupling between stimulating currents and recording artifacts, which allows us to estimate a multi-channel linear Wiener filter to predict and subsequently remove artifacts via subtraction. We confirm and verify the linearity assumption and demonstrate feasibility in a variety of recording modalities, including in vitro sciatic nerve stimulation, bilateral cochlear implant stimulation, and multi-channel stimulation and recording between the auditory midbrain and cortex. We demonstrate a vast enhancement in the recording quality with a typical artifact reduction of 25−40 dB. The method is efficient and can be scaled to arbitrary number of stimulus and recording sites, making it ideal for applications in large-scale arrays, closed-loop implants, and high-resolution multi-channel brain-machine interfaces. [ABSTRACT FROM AUTHOR]

Holographic representations of data encode information in packets of equal importance that enable progressive recovery. The quality of recovered data improves as more and more packets become available. This progressive recovery of the information is independent of the order in which packets become available. Such representations are ideally suited for distributed storage and for the transmission of data packets over networks with unpredictable delays and or erasures. Several methods for holographic representations of signals and images have been proposed over the years and multiple description information theory also deals with such representations. Surprisingly, however, these methods had not been considered in the classical framework of optimal least-squares estimation theory, until very recently. We develop a least-squares approach to the design of holographic representation for stochastic data vectors, relying on the framework widely used in modeling signals and images. [ABSTRACT FROM AUTHOR]

Spectral factorization is an operation which appears in many different engineering applications. This paper studies whether spectral factorization can be algorithmically computed on an abstract machine (a Turing machine). It is shown that there exist computable spectral densities with very good analytic properties (i.e. smooth with finite energy) such that the corresponding spectral factor cannot be determined on a Turing machine. Further, it will be proved that it is impossible to decide algorithmically whether or not a given computable density possesses a computable spectral factor. This negative result has consequences for applications of spectral factorization in computer-aided design, because there it is necessary that this problem be decidable. Conversely, this paper will show that if the logarithm of a computable spectral density belongs to certain Sobolev space of sufficiently smooth functions, then the spectral factor is always computable. As an application, the paper discusses the possibility of calculating the optimal causal Wiener filter on an abstract machine. [ABSTRACT FROM AUTHOR]

In a motor brain-machine interface system, since the electroencephalogram signal is changing through out the process of the arm movement, the offline trained decoder with fixed weights is often unable to convert the electroencephalogram signal accurately, resulting in poor recovery of joint motor function. In this paper, a receding horizon optimization strategy is chosen to online update the decoder weights and design an adaptive Wiener-filter-based decoder. Firstly, a classical Wiener-filter-based decoder with fixed weights is brief reviewed. Secondly, the weights in Wiener-filter-based decoder are updated by minimizing the cost function, which is composed by the sum of squared position errors in the given horizon at each sampling time. The simulation shows that the recovery effect of joint motor function and neuron activity in the BMI system with the adaptive decoder are both better than that in the BMI system with the fixed decoder. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new approach to the study of filtering theory by allowing the system's parameters to have a random character. We use Hida's white noise space theory to give an alternative characterization and a proper generalization to the Wiener filter over a suitable space of stochastic distributions introduced by Kondratiev. The main idea throughout this paper is to use the nuclearity of this space in order to view the random variables as bounded multiplication operators (with respect to the Wick product) between Hilbert spaces of stochastic distributions. This allows us to use operator theory tools and properties of Wiener algebras over Banach spaces to proceed and characterize the Wiener filter equations under the underlying randomness assumptions. [ABSTRACT FROM AUTHOR]

A brain tumor occurs when abnormal cells form within the brain. Many people suffer from brain tumor, and it is a serious and dangerous disease. The detection and classification of brain tumor is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. But, as the data in MRI images are of highquality, tumor detection and classification are very hard in this process. Medical imaging plays a major role in properly diagnosing the disease, wherein an essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision of the yields while also decreasing the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification process includes image acquisition, image pre-processing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using Weiner and median filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co-occurrence Matrix (GLCM) features are used. In the last stage, images are classified either as tumorous or non-tumorous. Classification is done using Genetic Algorithm Support Vector Machine (GA-SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on GA-SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy and computational time of these classifiers, CNN would provide high accuracy and GASVM with lesser simulation time. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using SPL dataset, which consists of 20 cases with 40 image samples of T2 FLAIR weighted MRI image and implemented using MATLAB software. [ABSTRACT FROM AUTHOR]

In this study we propose a system that uses image processing techniques to predict the growth rate, fetal head recognition and head length from fetal image. In the processing of images, we use various methods to improve the image by pre-processing and to try to extract other features from the image. Pre-processing, segmentation, and extraction of features are the three general steps that all types of data will undergo while using the technique. This work focuses primarily on developing a system for predicting whether fetal growth is normal or abnormal and extracting features such as fetal duration and BPD (Biparieatal Diameter). We are doing the research trimester-wise processing of ultrasound images from various pregnant ladies in our proposed job. Using wiener filter, the ultrasound images are smoothed and the image is segmented by border cleaning thresholding, and morphological operations. [ABSTRACT FROM AUTHOR]

Most speech enhancement algorithms focus on obtaining an estimator relying on stochastic models. In this paper, a minimum mean-square error (MMSE) estimator under a stochastic-deterministic model is proposed where a heavy-tail distribution called t- Location-Scale (tls) is used for modeling Discrete Fourier Transform coefficients of clean speech signals and exponential and sinusoidal models are employed as deterministic models. In the exponential model, the frequency and damping coefficient are estimated by using the Matrix Pencil method. Also, in previous studies, the number of exponential components in the deterministic model for stochastic-deterministic speech enhancement algorithm has been considered to be one. In this paper, the corresponding exponential model is developed to have an arbitrary number of exponential components. The speech enhancement experiments are performed in three modes, exponential-Gaussian (the first proposed method), exponential-tls (the second proposed method), and sinusoidal- Gaussian. Comparisons are made with the exponential-Gaussian method (with only one exponential component), as well as with the Weiner and tls stochastic estimators. The implementation results in the presence of six noise types from Noisex-92 dataset show that the two proposed methods improve the segSNR values and have quite similar PESQ values comparing with the stochastic based speech enhancement methods. [ABSTRACT FROM AUTHOR]

Images Quality assessment shows vital role in image processing applications and is an active field of research. In medical imaging field various imaging modalities i.e. Ultrasound (US), X-ray, Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) etc. are used to diagnose the disease by the doctors. In US imaging modality coherent sources are used for imaging purpose. Due to coherent sources, speckle noise inserts in US images and causes loss of information and blurring which leads to misdiagnose of the patients by the doctors. To avoid this situation, various despeckling filters are used to remove the speckle noise and their performance is analysed on the basis of subjective and objective assessment parameters. This paper summarizes the various performance evaluation parameters i.e MSE, RMSE, SNR, PSNR, AD, SI, NK, MD, LMSE, NAE, IQI, SSIM, and BETA, their significance & relevance with the example of despeckling filters i.e. LEE Sigma, Detail Preserving Anisotropic Diffusion (DPAD), Fourier Butterworth Filter (FBF), ROFTV (Rudin, Osher and Fatem) and Hybridized Mean and Wiener Filter. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) images are difficult to analyze due to the presence of speckle noise. Speckle noise must be filtered out before applying to other image processing applications. Three-layered feed forward back propagation neural network (TLFFBPNN) has been proposed to suppress the speckle noise. Gray-level co-occurrence matrix properties have been extracted, and back propagation training algorithm is used to train the neural network. The performance metrics such as peak signal to noise (PSNR), structural similarity index matrix (SSIM), edge preservation index (EPI), equivalent number of looks (ENL), and speckle suppression index (SSI) have been evaluated to find the efficiency of TLFFBPNN and compared with four recently developed de-speckling techniques. The exploratory outcomes show that the TLFFBPNN method has better de-speckling execution with great edge preservation. The comparative outcome reveals that the proposed TLFFBPNN de-speckled method outperformed in terms of PSNR of 0.98%, SSIM of 1.0%, SSI of 2.0%, EPI of 0.84%, and ENL of 0.5% when compared with the Wiener Filter Sparse Optimization in Contourlet transform domain de-speckling method. [ABSTRACT FROM AUTHOR]

Brain tumor identification is difficult task in the early stage of life. But now it has been improved with various machine learning algorithms. Now-a-days issue of brain tumor automatic identification is of great curious. In Order to discover the brain tumor of a patient, the data like MRI images of a patient's brain is used. Here our problem is to identify whether tumor is present or not in patients brain. It is very important to detect the tumors at starting level for a healthy life of patient. There are many literatures on discovering these kinds of brain tumors and improving the detection accuracies. In this paper, we estimate the brain tumor seriousness using Convolutional Neural Network(CNN) algorithm, which gives us accurate results. [ABSTRACT FROM AUTHOR]

Speech signals are usually affected by noises during the communication process. For suppressing the noise signal that is combined with the speech signal, a Wiener filter is adapted in digital hearing aids. Weiner filter plays an important role in noise suppression and enhancement by estimating the relation between the power spectra of the noise-affected speech signal and the noise signal. Power consumption and the hardware requirement are the important problems in adapting Weiner filter for major communication systems. In this work, we implemented an efficient Wiener filter and applied it for noise suppression along with a real-valued fast Fourier transform (FFT)/real-valued inverse FFT processor in digital hearing aids. The pipelined process was adopted for increasing the performance of the system. The proposed Wiener filter was designed to remove the iteration problems in the conventional Wiener filter. The division operation was replaced by an efficient inverse and multiplication operation in the proposed design. A modified architecture for matrix inversion with low computation complexity was implemented. The complete design computation was based on IEEE-754 standard single-precision floating-point numbers. The Wiener filter and the whole system architecture was implemented and designed on a Field Programmable Gate Array platform and simulated to validate the results in Xilinx ISE tools. An efficient reduction in power and area was obtained by adapting the proposed method for speech signal noise degradation. The performance of the proposed design was found to be 50.01% more efficient than that of existing designs. [ABSTRACT FROM AUTHOR]

Bu çalışmada poplin kumaşta hata tespiti için wiener filtre tabanlı bilgisayar destekli tespit sistemi sunulmaktadır. Hatalı kumaş görüntüleri dijital kamera yardımıyla alınmıştır. Geliştirilen sistem önişleme, bölütleme ve kumaş hata tespiti olarak 3 asamadan oluşmaktadır. On isleme aşaması, dijital görüntülere RGB seviyeden gri seviyeye çevirme ve görüntü iyileştirme adımlarından oluşmaktadır. Bölütleme aşamasında, gri seviye görüntünün arka planı morfolojik işlemler ile bölütlenmiştir. Ardından, bölütlenen görüntü, hata tespit işlemini gerçekleştirmek için ikili seviye görüntüye çevrilmiştir. Kumaş hata tespiti, hata tespit sistemi sırasında wiener filtre kullanılarak gerçeklenmiştir. Wiener filtre ikili seviyedeki görüntüye hatalı olmayan alanların elimine edilmesi amacıyla uygulanmıştır. Gerçeklenen sistem, tespit işlemi için hatalı poplin kumaşlara uygulanmıştır. Elde edilen sonuçlar, geliştirilen algoritmanın farklı tip kumaş hatalarında iyi sonuçlar vermektedir.